diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 00000000..666f331f --- /dev/null +++ b/.dockerignore @@ -0,0 +1,215 @@ +# Repo-specific DockerIgnore ------------------------------------------------------------------------------------------- +#.git +.cache +.idea +runs +output +coco +storage.googleapis.com + +data/samples/* +**/results*.txt +*.jpg + +# Neural Network weights ----------------------------------------------------------------------------------------------- +**/*.weights +**/*.pt +**/*.pth +**/*.onnx +**/*.mlmodel +**/*.torchscript + + +# Below Copied From .gitignore ----------------------------------------------------------------------------------------- +# Below Copied From .gitignore ----------------------------------------------------------------------------------------- + + +# GitHub Python GitIgnore ---------------------------------------------------------------------------------------------- +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv* +venv*/ +ENV*/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + + +# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore ----------------------------------------------- + +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon +Icon? + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + + +# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff: +.idea/* +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/dictionaries +.html # Bokeh Plots +.pg # TensorFlow Frozen Graphs +.avi # videos + +# Sensitive or high-churn files: +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml + +# Gradle: +.idea/**/gradle.xml +.idea/**/libraries + +# CMake +cmake-build-debug/ +cmake-build-release/ + +# Mongo Explorer plugin: +.idea/**/mongoSettings.xml + +## File-based project format: +*.iws + +## Plugin-specific files: + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 00000000..dad4239e --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +# this drop notebooks from GitHub language stats +*.ipynb linguist-vendored diff --git a/.github/ISSUE_TEMPLATE/--bug-report.md b/.github/ISSUE_TEMPLATE/--bug-report.md new file mode 100644 index 00000000..f29a7a59 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/--bug-report.md @@ -0,0 +1,55 @@ +--- +name: "\U0001F41BBug report" +about: Create a report to help us improve +title: '' +labels: bug +assignees: '' + +--- + +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: + - **Current repo**: run `git fetch && git status -uno` to check and `git pull` to update repo + - **Common dataset**: coco.yaml or coco128.yaml + - **Common environment**: Colab, Google Cloud, or Docker image. See https://github.com/ultralytics/yolov5#environments + +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()`. + + +## 🐛 Bug +A clear and concise description of what the bug is. + + +## To Reproduce (REQUIRED) + +Input: +``` +import torch + +a = torch.tensor([5]) +c = a / 0 +``` + +Output: +``` +Traceback (most recent call last): + File "/Users/glennjocher/opt/anaconda3/envs/env1/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3331, in run_code + exec(code_obj, self.user_global_ns, self.user_ns) + File "", line 5, in + c = a / 0 +RuntimeError: ZeroDivisionError +``` + + +## Expected behavior +A clear and concise description of what you expected to happen. + + +## Environment +If applicable, add screenshots to help explain your problem. + + - OS: [e.g. Ubuntu] + - GPU [e.g. 2080 Ti] + + +## Additional context +Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/--feature-request.md b/.github/ISSUE_TEMPLATE/--feature-request.md new file mode 100644 index 00000000..b16020d2 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/--feature-request.md @@ -0,0 +1,27 @@ +--- +name: "\U0001F680Feature request" +about: Suggest an idea for this project +title: '' +labels: enhancement +assignees: '' + +--- + +## 🚀 Feature + + +## Motivation + + + +## Pitch + + + +## Alternatives + + + +## Additional context + + diff --git a/.github/ISSUE_TEMPLATE/-question.md b/.github/ISSUE_TEMPLATE/-question.md new file mode 100644 index 00000000..2c22aea7 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/-question.md @@ -0,0 +1,13 @@ +--- +name: "❓Question" +about: Ask a general question +title: '' +labels: question +assignees: '' + +--- + +## ❔Question + + +## Additional context diff --git a/.github/workflows/ci-testing.yml b/.github/workflows/ci-testing.yml new file mode 100644 index 00000000..a0905fcc --- /dev/null +++ b/.github/workflows/ci-testing.yml @@ -0,0 +1,76 @@ +name: CI CPU testing + +on: # https://help.github.com/en/actions/reference/events-that-trigger-workflows + push: + pull_request: + schedule: + - cron: "0 0 * * *" + +jobs: + cpu-tests: + + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, macos-latest, windows-latest] + python-version: [3.8] + model: ['yolov5s'] # models to test + + # Timeout: https://stackoverflow.com/a/59076067/4521646 + timeout-minutes: 50 + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + + # Note: This uses an internal pip API and may not always work + # https://github.com/actions/cache/blob/master/examples.md#multiple-oss-in-a-workflow + - name: Get pip cache + id: pip-cache + run: | + python -c "from pip._internal.locations import USER_CACHE_DIR; print('::set-output name=dir::' + USER_CACHE_DIR)" + + - name: Cache pip + uses: actions/cache@v1 + with: + path: ${{ steps.pip-cache.outputs.dir }} + key: ${{ runner.os }}-${{ matrix.python-version }}-pip-${{ hashFiles('requirements.txt') }} + restore-keys: | + ${{ runner.os }}-${{ matrix.python-version }}-pip- + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -qr requirements.txt -f https://download.pytorch.org/whl/cpu/torch_stable.html + pip install -q onnx + python --version + pip --version + pip list + shell: bash + + - name: Download data + run: | + # curl -L -o tmp.zip https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip + # unzip -q tmp.zip -d ../ + # rm tmp.zip + + - name: Tests workflow + run: | + # export PYTHONPATH="$PWD" # to run '$ python *.py' files in subdirectories + di=cpu # inference devices # define device + + # train + python train.py --img 256 --batch 8 --weights weights/${{ matrix.model }}.pt --cfg models/${{ matrix.model }}.yaml --epochs 1 --device $di + # detect + python detect.py --weights weights/${{ matrix.model }}.pt --device $di + python detect.py --weights runs/exp0/weights/last.pt --device $di + # test + python test.py --img 256 --batch 8 --weights weights/${{ matrix.model }}.pt --device $di + python test.py --img 256 --batch 8 --weights runs/exp0/weights/last.pt --device $di + + python models/yolo.py --cfg models/${{ matrix.model }}.yaml # inspect + python models/export.py --img 256 --batch 1 --weights weights/${{ matrix.model }}.pt # export + shell: bash diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml new file mode 100644 index 00000000..24919d5b --- /dev/null +++ b/.github/workflows/greetings.yml @@ -0,0 +1,35 @@ +name: Greetings + +on: [pull_request_target, issues] + +jobs: + greeting: + runs-on: ubuntu-latest + steps: + - uses: actions/first-interaction@v1 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + pr-message: | + Hello @${{ github.actor }}, thank you for submitting a PR! To allow your work to be integrated as seamlessly as possible, we advise you to: + - Verify your PR is **up-to-date with origin/master.** If your PR is behind origin/master update by running the following, replacing 'feature' with the name of your local branch: + ```bash + git remote add upstream https://github.com/ultralytics/yolov5.git + git fetch upstream + git checkout feature # <----- replace 'feature' with local branch name + git rebase upstream/master + git push -u origin -f + ``` + - Verify all Continuous Integration (CI) **checks are passing**. + - 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 + + issue-message: | + Hello @${{ github.actor }}, thank you for your interest in our work! Please visit our [Custom Training Tutorial](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) to get started, and see our [Jupyter Notebook](https://github.com/ultralytics/yolov5/blob/master/tutorial.ipynb) Open In Colab, [Docker Image](https://hub.docker.com/r/ultralytics/yolov5), and [Google Cloud Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) for example environments. + + If this is a bug report, please provide screenshots and **minimum viable code to reproduce your issue**, otherwise we can not help you. + + If this is a custom model or data training question, please note 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: + - **Cloud-based AI** systems operating on **hundreds of HD video streams in realtime.** + - **Edge AI** integrated into custom iOS and Android apps for realtime **30 FPS video inference.** + - **Custom data training**, hyperparameter evolution, and model exportation to any destination. + + For more information please visit https://www.ultralytics.com. diff --git a/.github/workflows/rebase.yml b/.github/workflows/rebase.yml new file mode 100644 index 00000000..e86c5774 --- /dev/null +++ b/.github/workflows/rebase.yml @@ -0,0 +1,21 @@ +name: Automatic Rebase +# https://github.com/marketplace/actions/automatic-rebase + +on: + issue_comment: + types: [created] + +jobs: + rebase: + name: Rebase + if: github.event.issue.pull_request != '' && contains(github.event.comment.body, '/rebase') + runs-on: ubuntu-latest + steps: + - name: Checkout the latest code + uses: actions/checkout@v2 + with: + fetch-depth: 0 + - name: Automatic Rebase + uses: cirrus-actions/rebase@1.3.1 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml new file mode 100644 index 00000000..d4126b8b --- /dev/null +++ b/.github/workflows/stale.yml @@ -0,0 +1,18 @@ +name: Close stale issues +on: + schedule: + - cron: "0 0 * * *" + +jobs: + stale: + runs-on: ubuntu-latest + steps: + - uses: actions/stale@v1 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + stale-issue-message: 'This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.' + stale-pr-message: 'This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.' + days-before-stale: 30 + days-before-close: 5 + exempt-issue-labels: 'documentation,tutorial' + operations-per-run: 100 # The maximum number of operations per run, used to control rate limiting. diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..db98bf07 --- /dev/null +++ b/.gitignore @@ -0,0 +1,245 @@ +# Repo-specific GitIgnore ---------------------------------------------------------------------------------------------- +*.jpg +*.jpeg +*.png +*.bmp +*.tif +*.tiff +*.heic +*.JPG +*.JPEG +*.PNG +*.BMP +*.TIF +*.TIFF +*.HEIC +*.mp4 +*.mov +*.MOV +*.avi +*.data +*.json + +*.cfg +!cfg/yolov3*.cfg + +storage.googleapis.com +runs/* +data/* +!data/samples/zidane.jpg +!data/samples/bus.jpg +!data/coco.names +!data/coco_paper.names +!data/coco.data +!data/coco_*.data +!data/coco_*.txt +!data/trainvalno5k.shapes +!data/*.sh + +pycocotools/* +results*.txt +gcp_test*.sh + +# MATLAB GitIgnore ----------------------------------------------------------------------------------------------------- +*.m~ +*.mat +!targets*.mat + +# Neural Network weights ----------------------------------------------------------------------------------------------- +*.weights +*.pt +*.onnx +*.mlmodel +*.torchscript +darknet53.conv.74 +yolov3-tiny.conv.15 + +# GitHub Python GitIgnore ---------------------------------------------------------------------------------------------- +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv* +venv*/ +ENV*/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + + +# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore ----------------------------------------------- + +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon +Icon? + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + + +# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff: +.idea/* +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/dictionaries +.html # Bokeh Plots +.pg # TensorFlow Frozen Graphs +.avi # videos + +# Sensitive or high-churn files: +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml + +# Gradle: +.idea/**/gradle.xml +.idea/**/libraries + +# CMake +cmake-build-debug/ +cmake-build-release/ + +# Mongo Explorer plugin: +.idea/**/mongoSettings.xml + +## File-based project format: +*.iws + +## Plugin-specific files: + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties diff --git "a/DOTA_demo_view/\350\256\260\345\276\227\344\270\213\350\275\275\346\274\224\347\244\272\346\226\207\344\273\266.txt" "b/DOTA_demo_view/\350\256\260\345\276\227\344\270\213\350\275\275\346\274\224\347\244\272\346\226\207\344\273\266.txt" new file mode 100644 index 00000000..e69de29b diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 00000000..59b1a03d --- /dev/null +++ b/Dockerfile @@ -0,0 +1,52 @@ +# Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch +FROM nvcr.io/nvidia/pytorch:20.09-py3 + +# Install dependencies +RUN pip install --upgrade pip +# COPY requirements.txt . +# RUN pip install -r requirements.txt +RUN pip install gsutil + +# Create working directory +RUN mkdir -p /usr/src/app +WORKDIR /usr/src/app + +# Copy contents +COPY . /usr/src/app + +# Copy weights +#RUN python3 -c "from models import *; \ +#attempt_download('weights/yolov5s.pt'); \ +#attempt_download('weights/yolov5m.pt'); \ +#attempt_download('weights/yolov5l.pt')" + + +# --------------------------------------------------- Extras Below --------------------------------------------------- + +# Build and Push +# t=ultralytics/yolov5:latest && sudo docker build -t $t . && sudo docker push $t +# for v in {300..303}; do t=ultralytics/coco:v$v && sudo docker build -t $t . && sudo docker push $t; done + +# Pull and Run +# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host $t + +# Pull and Run with local directory access +# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/coco:/usr/src/coco $t + +# Kill all +# sudo docker kill $(sudo docker ps -q) + +# Kill all image-based +# sudo docker kill $(sudo docker ps -a -q --filter ancestor=ultralytics/yolov5:latest) + +# Bash into running container +# sudo docker container exec -it ba65811811ab bash + +# Bash into stopped container +# sudo docker commit 092b16b25c5b usr/resume && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/coco:/usr/src/coco --entrypoint=sh usr/resume + +# Send weights to GCP +# python -c "from utils.general import *; strip_optimizer('runs/exp0_*/weights/best.pt', 'tmp.pt')" && gsutil cp tmp.pt gs://*.pt + +# Clean up +# docker system prune -a --volumes diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..9e419e04 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ +GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. \ No newline at end of file diff --git a/detect.py b/detect.py new file mode 100644 index 00000000..db35548d --- /dev/null +++ b/detect.py @@ -0,0 +1,252 @@ +import argparse +import os +import platform +import shutil +import time +from pathlib import Path + +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_labels, + xyxy2xywh, plot_one_rotated_box, strip_optimizer, set_logging, rotate_non_max_suppression) +from utils.torch_utils import select_device, load_classifier, time_synchronized +from utils.evaluation_utils import rbox2txt + + +def detect(save_img=False): + ''' + input: save_img_flag + output(result): + ''' + # 获取输出文件夹，输入路径，权重，参数等参数 + out, source, weights, view_img, save_txt, imgsz = \ + opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size + webcam = source.isnumeric() or source.startswith(('rtsp://', 'rtmp://', 'http://')) or source.endswith('.txt') + + # Initialize + set_logging() + # 获取设备 + device = select_device(opt.device) + # 移除之前的输出文件夹,并新建输出文件夹 + if os.path.exists(out): + shutil.rmtree(out) # delete output folder + os.makedirs(out) # make new output folder + # 如果设备为gpu，使用Float16 + half = device.type != 'cpu' # half precision only supported on CUDA + + # Load model + # 加载Float32模型，确保用户设定的输入图片分辨率能整除最大步长s=32(如不能则调整为能整除并返回) + ''' + model = Model( + (model): Sequential( + (0): Focus(...) + (1): Conv(...) + ... + (24): Detect(...) + ) + ''' + model = attempt_load(weights, map_location=device) # load FP32 model + imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size + + # 设置Float16 + if half: + model.half() # to FP16 + + # Second-stage classifier + classify = False + if classify: + modelc = 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() + + # Set Dataloader + # 通过不同的输入源来设置不同的数据加载方式 + vid_path, vid_writer = None, None + if webcam: + view_img = True + 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 + # 获取类别名字 names = ['person', 'bicycle', 'car',...,'toothbrush'] + names = model.module.names if hasattr(model, 'module') else model.names + # 设置画框的颜色 colors = [[178, 63, 143], [25, 184, 176], [238, 152, 129],....,[235, 137, 120]]随机设置RGB颜色 + colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))] + + # Run inference + t0 = time.time() + # 进行一次前向推理,测试程序是否正常 向量维度（1，3，imgsz，imgsz） + 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 图片/视频路径 'E:\...\bus.jpg' + img 进行resize+pad之后的图片 1*3*re_size1*resize2的张量 (3,img_height,img_weight) + img0 原size图片 (img_height,img_weight,3) + cap 当读取图片时为None，读取视频时为视频源 + """ + for path, img, im0s, vid_cap in dataset: + print(img.shape) + img = torch.from_numpy(img).to(device) + # 图片也设置为Float16 + img = img.half() if half else img.float() # uint8 to fp16/32 + img /= 255.0 # 0 - 255 to 0.0 - 1.0 + # 没有batch_size的话则在最前面添加一个轴 + if img.ndimension() == 3: + # (in_channels,size1,size2) to (1,in_channels,img_height,img_weight) + img = img.unsqueeze(0) # 在[0]维增加一个维度 + + # Inference + t1 = time_synchronized() + """ + model: + input: in_tensor (batch_size, 3, img_height, img_weight) + output: 推理时返回 [z,x] + z tensor: [small+medium+large_inference] size=(batch_size, 3 * (small_size1*small_size2 + medium_size1*medium_size2 + large_size1*large_size2), nc) + x list: [small_forward, medium_forward, large_forward] eg:small_forward.size=( batch_size, 3种scale框, size1, size2, [xywh,score,num_classes]) + ''' + + 前向传播 返回pred[0]的shape是(1, num_boxes, nc) + h,w为传入网络图片的长和宽，注意dataset在检测时使用了矩形推理，所以这里h不一定等于w + num_boxes = 3 * h/32 * w/32 + 3 * h/16 * w/16 + 3 * h/8 * w/8 + pred[0][..., 0:4] 预测框坐标为xywh(中心点+宽长)格式 + pred[0][..., 4]为objectness置信度 + pred[0][..., 5:5+nc]为分类结果 + pred[0][..., 5+nc:]为Θ分类结果 + """ + # pred : (batch_size, num_boxes, no) batch_size=1 + pred = model(img, augment=opt.augment)[0] + + # Apply NMS + # 进行NMS + # pred : list[tensor(batch_size, num_conf_nms, [xylsθ,conf,classid])] θ∈[0,179] + #pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) + pred = rotate_non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms, without_iouthres=False) + t2 = time_synchronized() + + # Apply Classifier + if classify: + pred = apply_classifier(pred, modelc, img, im0s) + + # Process detections + for i, det in enumerate(pred): # i:image index det:(num_nms_boxes, [xylsθ,conf,classid]) θ∈[0,179] + if webcam: # batch_size >= 1 + p, s, im0 = path[i], '%g: ' % i, im0s[i].copy() + else: + p, s, im0 = path, '', im0s + + save_path = str(Path(out) / Path(p).name) # 图片保存路径+图片名字 + txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '') + #print(txt_path) + 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 img_size to im0 size + det[:, :5] = scale_labels(img.shape[2:], det[:, :5], im0.shape).round() + + # Print results det:(num_nms_boxes, [xylsθ,conf,classid]) θ∈[0,179] + for c in det[:, -1].unique(): # unique函数去除其中重复的元素，并按元素（类别）由大到小返回一个新的无元素重复的元组或者列表 + n = (det[:, -1] == c).sum() # detections per class 每个类别检测出来的素含量 + s += '%g %ss, ' % (n, names[int(c)]) # add to string 输出‘数量 类别,’ + + # Write results det:(num_nms_boxes, [xywhθ,conf,classid]) θ∈[0,179] + for *rbox, conf, cls in reversed(det): # 翻转list的排列结果,改为类别由小到大的排列 + # rbox=[tensor(x),tensor(y),tensor(w),tensor(h),tsneor(θ)] θ∈[0,179] + # if save_txt: # Write to file + # xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + # with open(txt_path + '.txt', 'a') as f: + # f.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format + + if save_img or view_img: # Add bbox to image + label = '%s %.2f' % (names[int(cls)], conf) + classname = '%s' % names[int(cls)] + conf_str = '%.3f' % conf + rbox2txt(rbox, classname, conf_str, Path(p).stem, str(out + '/result_txt/result_before_merge')) + #plot_one_box(rbox, im0, label=label, color=colors[int(cls)], line_thickness=2) + plot_one_rotated_box(rbox, im0, label=label, color=colors[int(cls)], line_thickness=1, + pi_format=False) + + # Print time (inference + NMS) + print('%sDone. (%.3fs)' % (s, t2 - t1)) + + # Stream results 播放结果 + if view_img: + cv2.imshow(p, im0) + if cv2.waitKey(1) == ord('q'): # q to quit + raise StopIteration + + # Save results (image with detections) + if save_img: + if dataset.mode == 'images': + cv2.imwrite(save_path, im0) + pass + else: + if vid_path != save_path: # new video + vid_path = save_path + 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(*fourcc), fps, (w, h)) + vid_writer.write(im0) + + if save_txt or save_img: + print(' Results saved to %s' % Path(out)) + + print(' All Done. (%.3fs)' % (time.time() - t0)) + + +if __name__ == '__main__': + """ + weights:训练的权重 + source:测试数据，可以是图片/视频路径，也可以是'0'(电脑自带摄像头),也可以是rtsp等视频流 + output:网络预测之后的图片/视频的保存路径 + img-size:网络输入图片大小 + conf-thres:置信度阈值 + iou-thres:做nms的iou阈值 + device:设置设备 + view-img:是否展示预测之后的图片/视频，默认False + save-txt:是否将预测的框坐标以txt文件形式保存，默认False + classes:设置只保留某一部分类别，形如0或者0 2 3 + agnostic-nms:进行nms是否将所有类别框一视同仁，默认False + augment:推理的时候进行多尺度，翻转等操作(TTA)推理 + update:如果为True，则对所有模型进行strip_optimizer操作，去除pt文件中的优化器等信息，默认为False + """ + parser = argparse.ArgumentParser() + parser.add_argument('--weights', nargs='+', type=str, default='./weights/YOLOv5_DOTA_OBB.pt', help='model.pt path(s)') + parser.add_argument('--source', type=str, default='DOTA_demo_view/images', help='source') # file/folder, 0 for webcam + parser.add_argument('--output', type=str, default='DOTA_demo_view/detection', help='output folder') # output folder + parser.add_argument('--img-size', type=int, default=1024, 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=0.4, help='IOU threshold for NMS') + parser.add_argument('--device', default='0,1', 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: --class 0, or --class 0 2 3') + parser.add_argument('--agnostic-nms', action='store_true', default=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') + opt = parser.parse_args() + print(opt) + + with torch.no_grad(): + if opt.update: # update all models (to fix SourceChangeWarning) + for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']: + detect() + # 去除pt文件中的优化器等信息 + strip_optimizer(opt.weights) + else: + detect() diff --git a/evaluation.py b/evaluation.py new file mode 100644 index 00000000..37120fd0 --- /dev/null +++ b/evaluation.py @@ -0,0 +1,367 @@ +# -------------------------------------------------------- +# dota_evaluation_task1 +# Licensed under The MIT License [see LICENSE for details] +# Written by Jian Ding, based on code from Bharath Hariharan +# -------------------------------------------------------- + +""" + To use the code, users should to config detpath, annopath and imagesetfile + detpath is the path for 15 result files, for the format, you can refer to "http://captain.whu.edu.cn/DOTAweb/tasks.html" + search for PATH_TO_BE_CONFIGURED to config the paths + Note, the evaluation is on the large scale images +""" +import xml.etree.ElementTree as ET +import os +#import cPickle +import numpy as np +import matplotlib.pyplot as plt +from utils import polyiou +from functools import partial +import pdb +from utils.evaluation_utils import mergebypoly, evaluation_trans, image2txt, draw_DOTA_image + +def parse_gt(filename): + """ + + :param filename: ground truth file to parse + :return: all instances in a picture + """ + objects = [] + with open(filename, 'r') as f: + while True: + line = f.readline() + if line: + splitlines = line.strip().split(' ') + object_struct = {} + if (len(splitlines) < 9): + continue + object_struct['name'] = splitlines[8] + + # if (len(splitlines) == 9): + # object_struct['difficult'] = 0 + # elif (len(splitlines) == 10): + # object_struct['difficult'] = int(splitlines[9]) + object_struct['difficult'] = 0 + object_struct['bbox'] = [float(splitlines[0]), + float(splitlines[1]), + float(splitlines[2]), + float(splitlines[3]), + float(splitlines[4]), + float(splitlines[5]), + float(splitlines[6]), + float(splitlines[7])] + objects.append(object_struct) + else: + break + return objects +def voc_ap(rec, prec, use_07_metric=False): + """ ap = voc_ap(rec, prec, [use_07_metric]) + Compute VOC AP given precision and recall. + If use_07_metric is true, uses the + VOC 07 11 point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + +def voc_eval(detpath, + annopath, + imagesetfile, + classname, + # cachedir, + ovthresh=0.5, + use_07_metric=False): + """rec, prec, ap = voc_eval(detpath, + annopath, + imagesetfile, + classname, + [ovthresh], + [use_07_metric]) + Top level function that does the PASCAL VOC evaluation. + detpath: Path to detections + detpath.format(classname) should produce the detection results file. + annopath: Path to annotations + annopath.format(imagename) should be the xml annotations file. + imagesetfile: Text file containing the list of images, one image per line. + classname: Category name (duh) + cachedir: Directory for caching the annotations + [ovthresh]: Overlap threshold (default = 0.5) + [use_07_metric]: Whether to use VOC07's 11 point AP computation + (default False) + """ + # assumes detections are in detpath.format(classname) + # assumes annotations are in annopath.format(imagename) + # assumes imagesetfile is a text file with each line an image name + # cachedir caches the annotations in a pickle file + + # first load gt + #if not os.path.isdir(cachedir): + # os.mkdir(cachedir) + #cachefile = os.path.join(cachedir, 'annots.pkl') + # read list of images + with open(imagesetfile, 'r') as f: + lines = f.readlines() + imagenames = [x.strip() for x in lines] + #print('imagenames: ', imagenames) + #if not os.path.isfile(cachefile): + # load annots + recs = {} + for i, imagename in enumerate(imagenames): + #print('parse_files name: ', annopath.format(imagename)) + recs[imagename] = parse_gt(annopath.format(imagename)) + + # extract gt objects for this class + class_recs = {} + npos = 0 + for imagename in imagenames: + R = [obj for obj in recs[imagename] if obj['name'] == classname] + bbox = np.array([x['bbox'] for x in R]) + difficult = np.array([x['difficult'] for x in R]).astype(np.bool) + det = [False] * len(R) + npos = npos + sum(~difficult) + class_recs[imagename] = {'bbox': bbox, + 'difficult': difficult, + 'det': det} + + # read dets from Task1* files + detfile = detpath.format(classname) + with open(detfile, 'r') as f: + lines = f.readlines() + + splitlines = [x.strip().split(' ') for x in lines] + image_ids = [x[0] for x in splitlines] + confidence = np.array([float(x[1]) for x in splitlines]) + + #print('check confidence: ', confidence) + + BB = np.array([[float(z) for z in x[2:]] for x in splitlines]) + + # sort by confidence + sorted_ind = np.argsort(-confidence) + sorted_scores = np.sort(-confidence) + + #print('check sorted_scores: ', sorted_scores) + #print('check sorted_ind: ', sorted_ind) + + ## note the usage only in numpy not for list + BB = BB[sorted_ind, :] + image_ids = [image_ids[x] for x in sorted_ind] + #print('check imge_ids: ', image_ids) + #print('imge_ids len:', len(image_ids)) + # go down dets and mark TPs and FPs + nd = len(image_ids) + tp = np.zeros(nd) + fp = np.zeros(nd) + for d in range(nd): + R = class_recs[image_ids[d]] + bb = BB[d, :].astype(float) + ovmax = -np.inf + BBGT = R['bbox'].astype(float) + + ## compute det bb with each BBGT + + if BBGT.size > 0: + # compute overlaps + # intersection + + # 1. calculate the overlaps between hbbs, if the iou between hbbs are 0, the iou between obbs are 0, too. + # pdb.set_trace() + BBGT_xmin = np.min(BBGT[:, 0::2], axis=1) + BBGT_ymin = np.min(BBGT[:, 1::2], axis=1) + BBGT_xmax = np.max(BBGT[:, 0::2], axis=1) + BBGT_ymax = np.max(BBGT[:, 1::2], axis=1) + bb_xmin = np.min(bb[0::2]) + bb_ymin = np.min(bb[1::2]) + bb_xmax = np.max(bb[0::2]) + bb_ymax = np.max(bb[1::2]) + + ixmin = np.maximum(BBGT_xmin, bb_xmin) + iymin = np.maximum(BBGT_ymin, bb_ymin) + ixmax = np.minimum(BBGT_xmax, bb_xmax) + iymax = np.minimum(BBGT_ymax, bb_ymax) + iw = np.maximum(ixmax - ixmin + 1., 0.) + ih = np.maximum(iymax - iymin + 1., 0.) + inters = iw * ih + + # union + uni = ((bb_xmax - bb_xmin + 1.) * (bb_ymax - bb_ymin + 1.) + + (BBGT_xmax - BBGT_xmin + 1.) * + (BBGT_ymax - BBGT_ymin + 1.) - inters) + + overlaps = inters / uni + + BBGT_keep_mask = overlaps > 0 + BBGT_keep = BBGT[BBGT_keep_mask, :] + BBGT_keep_index = np.where(overlaps > 0)[0] + # pdb.set_trace() + def calcoverlaps(BBGT_keep, bb): + overlaps = [] + for index, GT in enumerate(BBGT_keep): + + overlap = polyiou.iou_poly(polyiou.VectorDouble(BBGT_keep[index]), polyiou.VectorDouble(bb)) + overlaps.append(overlap) + return overlaps + if len(BBGT_keep) > 0: + overlaps = calcoverlaps(BBGT_keep, bb) + + ovmax = np.max(overlaps) + jmax = np.argmax(overlaps) + # pdb.set_trace() + jmax = BBGT_keep_index[jmax] + if ovmax > ovthresh: + if not R['difficult'][jmax]: + if not R['det'][jmax]: + tp[d] = 1. + R['det'][jmax] = 1 + else: + fp[d] = 1. + else: + fp[d] = 1. + + # compute precision recall + + print('check fp:', fp) + print('check tp', tp) + + + print('npos num:', npos) + fp = np.cumsum(fp) + tp = np.cumsum(tp) + + rec = tp / float(npos) + # avoid divide by zero in case the first detection matches a difficult + # ground truth + prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) + ap = voc_ap(rec, prec, use_07_metric) + + return rec, prec, ap + +def evaluation(detoutput, imageset, annopath, classnames): + """ + 评估程序 + @param detoutput: detect.py函数的结果存放输出路径 + @param imageset: # val DOTA原图数据集图像路径 + @param annopath: 'r/.../{:s}.txt' 原始val测试集的DOTAlabels路径 + @param classnames: 测试集中的目标种类 + """ + result_before_merge_path = str(detoutput + '/result_txt/result_before_merge') + result_merged_path = str(detoutput + '/result_txt/result_merged') + result_classname_path = str(detoutput + '/result_txt/result_classname') + imageset_name_file_path = str(detoutput + '/result_txt') + + # see demo for example + mergebypoly( + result_before_merge_path, + result_merged_path + ) + print('检测结果已merge') + evaluation_trans( + result_merged_path, + result_classname_path + ) + print('检测结果已按照类别分类') + image2txt( + imageset, # val原图数据集路径 + imageset_name_file_path + ) + print('校验数据集名称文件已生成') + + detpath = str(result_classname_path + '/Task1_{:s}.txt') # 'r/.../Task1_{:s}.txt' 存放各类别结果文件txt的路径 + annopath = annopath + imagesetfile = str(imageset_name_file_path +'/imgnamefile.txt') # 'r/.../imgnamefile.txt' 测试集图片名称txt + + # detpath = r'PATH_TO_BE_CONFIGURED/Task1_{:s}.txt' + # annopath = r'PATH_TO_BE_CONFIGURED/{:s}.txt' # change the directory to the path of val/labelTxt, if you want to do evaluation on the valset + # imagesetfile = r'PATH_TO_BE_CONFIGURED/valset.txt' + + # For DOTA-v1.5 + #classnames = ['plane', 'baseball-diamond', 'bridge', 'ground-track-field', 'small-vehicle', 'large-vehicle', 'ship', 'tennis-court', + # 'basketball-court', 'storage-tank', 'soccer-ball-field', 'roundabout', 'harbor', 'swimming-pool', 'helicopter', 'container-crane'] + # For DOTA-v1.0 + # classnames = ['plane', 'baseball-diamond', 'bridge', 'ground-track-field', 'small-vehicle', 'large-vehicle', 'ship', 'tennis-court', + # 'basketball-court', 'storage-tank', 'soccer-ball-field', 'roundabout', 'harbor', 'swimming-pool', 'helicopter', '] + classaps = [] + map = 0 + for classname in classnames: + print('classname:', classname) + rec, prec, ap = voc_eval(detpath, + annopath, + imagesetfile, + classname, + ovthresh=0.5, + use_07_metric=True) + map = map + ap + #print('rec: ', rec, 'prec: ', prec, 'ap: ', ap) + print('ap: ', ap) + classaps.append(ap) + + # umcomment to show p-r curve of each category + # plt.figure(figsize=(8,4)) + # plt.xlabel('recall') + # plt.ylabel('precision') + # plt.plot(rec, prec) + # plt.show() + map = map/len(classnames) + print('map:', map) + classaps = 100*np.array(classaps) + print('classaps: ', classaps) + + + +if __name__ == '__main__': + ''' + 计算AP的流程: + 1.detect.py检测一个文件夹的所有图片并把检测结果按照图片原始来源存入 原始图片名称.txt中: (rbox2txt函数) + txt中的内容格式: 目标所属图片名称_分割id 置信度 poly classname + 2.ResultMerge.py将所有 原始图片名称.txt 进行merge和nms,并将结果存入到另一个文件夹的 原始图片名称.txt中: + txt中的内容格式: 目标所属图片名称 置信度 poly classname + 3.写一个evaluation_trans.py将上个文件夹中的所有txt中的目标提取出来,按照目标类别分别存入 Task1_类别名.txt中: + txt中的内容格式: 目标所属原始图片名称 置信度 poly + 4.写一个imgname2txt.py 将测试集的所有图片名称打印到namefile.txt中 + ''' + # For DOTA-v1.5 + classnames = ['plane', 'baseball-diamond', 'bridge', 'ground-track-field', 'small-vehicle', 'large-vehicle', 'ship', 'tennis-court', + 'basketball-court', 'storage-tank', 'soccer-ball-field', 'roundabout', 'harbor', 'swimming-pool', 'helicopter', 'container-crane'] + # For DOTA-v1.0 + # classnames = ['plane', 'baseball-diamond', 'bridge', 'ground-track-field', 'small-vehicle', 'large-vehicle', 'ship', 'tennis-court', + # 'basketball-court', 'storage-tank', 'soccer-ball-field', 'roundabout', 'harbor', 'swimming-pool', 'helicopter', '] + + classnames_inVal = ['harbor', 'large-vehicle', 'ship', 'small-vehicle'] + # + evaluation( + detoutput='/home/test/Persons/hukaixuan/yolov5_DOTA_OBB/DOTA_demo_view/detection', + imageset=r'/home/test/Persons/hukaixuan/yolov5_DOTA_OBB/DOTA_demo_view/row_images', + annopath=r'/home/test/Persons/hukaixuan/yolov5_DOTA_OBB/DOTA_demo_view/row_DOTA_labels/{:s}.txt', + classnames=classnames_inVal + ) + + draw_DOTA_image(imgsrcpath=r'/home/test/Persons/hukaixuan/yolov5_DOTA_OBB/DOTA_demo_view/row_images', + imglabelspath=r'/home/test/Persons/hukaixuan/yolov5_DOTA_OBB/DOTA_demo_view/detection/result_txt/result_merged', + dstpath=r'/home/test/Persons/hukaixuan/yolov5_DOTA_OBB/DOTA_demo_view/detection/merged_drawed', + extractclassname=classnames, + thickness=2 + ) \ No newline at end of file diff --git a/hubconf.py b/hubconf.py new file mode 100644 index 00000000..168b4050 --- /dev/null +++ b/hubconf.py @@ -0,0 +1,103 @@ +"""File for accessing YOLOv5 via PyTorch Hub https://pytorch.org/hub/ + +Usage: + import torch + model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, channels=3, classes=80) +""" + +dependencies = ['torch', 'yaml'] +import os + +import torch + +from models.common import NMS +from models.yolo import Model +from utils.google_utils import attempt_download + + +def create(name, pretrained, channels, classes): + """Creates a specified YOLOv5 model + + Arguments: + name (str): name of model, i.e. 'yolov5s' + pretrained (bool): load pretrained weights into the model + channels (int): number of input channels + classes (int): number of model classes + + Returns: + pytorch model + """ + config = os.path.join(os.path.dirname(__file__), 'models', '%s.yaml' % name) # model.yaml path + try: + model = Model(config, channels, classes) + if pretrained: + ckpt = '%s.pt' % name # checkpoint filename + attempt_download(ckpt) # download if not found locally + state_dict = torch.load(ckpt, map_location=torch.device('cpu'))['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 + + model.add_nms() # add NMS module + model.eval() + return model + + except Exception as e: + help_url = 'https://github.com/ultralytics/yolov5/issues/36' + s = 'Cache maybe be out of date, deleting cache and retrying may solve this. See %s for help.' % help_url + raise Exception(s) from e + + +def yolov5s(pretrained=False, channels=3, classes=80): + """YOLOv5-small model from https://github.com/ultralytics/yolov5 + + 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('yolov5s', pretrained, channels, classes) + + +def yolov5m(pretrained=False, channels=3, classes=80): + """YOLOv5-medium model from https://github.com/ultralytics/yolov5 + + 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('yolov5m', pretrained, channels, classes) + + +def yolov5l(pretrained=False, channels=3, classes=80): + """YOLOv5-large model from https://github.com/ultralytics/yolov5 + + 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('yolov5l', pretrained, channels, classes) + + +def yolov5x(pretrained=False, channels=3, classes=80): + """YOLOv5-xlarge model from https://github.com/ultralytics/yolov5 + + 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('yolov5x', pretrained, channels, classes) diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/models/common.py b/models/common.py new file mode 100644 index 00000000..43803d24 --- /dev/null +++ b/models/common.py @@ -0,0 +1,195 @@ +# This file contains modules common to various models +import math + +import torch +import torch.nn as nn +from utils.general import non_max_suppression +''' +feature map尺寸计算公式： out_size = (in_size + 2*Padding - kernel_size)/strides + 1 +卷积计算时map尺寸向下取整 +池化计算时map尺寸向上取整 +''' + +def autopad(k, p=None): # kernel, padding + ''' + 自动填充 + 返回padding值 + kernel_size 为 int类型时 ：padding = k // 2（整数除法进行一次） + 否则 : padding = [x // 2 for x in k] + ''' + # Pad to 'same' + if p is None: # k是否为int类型，是则返回True + 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): + ''' + 深度分离卷积层 Depthwise convolution： + 是G（group）CONV的极端情况； + 分组数量等于输入通道数量，即每个通道作为一个小组分别进行卷积，结果联结作为输出，Cin = Cout = g，没有bias项。 + c1 : in_channels + c2 : out_channels + k : kernel_size + s : stride + act : 是否使用激活函数 + math.gcd() 返回的是最大公约数 + ''' + # Depthwise convolution + return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act) + +class Conv(nn.Module): + ''' + 标准卷积层Conv + 包括Conv2d + BN + HardWish激活函数 + (self, in_channels, out_channels, kernel_size, stride, padding, groups, activation_flag) + p=None时，out_size = in_size/strides + ''' + # 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.Hardswish() if act else nn.Identity() + + def forward(self, x): # 前向计算（有BN） + return self.act(self.bn(self.conv(x))) + + def fuseforward(self, x): # 前向融合计算（无BN） + return self.act(self.conv(x)) + +class Bottleneck(nn.Module): + ''' + 标准Bottleneck层 + input : input + output : input + Conv3×3（Conv1×1(input)） + (self, in_channels, out_channels, shortcut_flag, group, expansion隐藏神经元的缩放因子) + out_size = in_size + ''' + # 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): + ''' + 若 shortcut_flag为Ture 且 输入输出通道数相等，则返回跳接后的结构： + x + Conv3×3（Conv1×1(x)） + 否则不进行跳接： + Conv3×3（Conv1×1(x)） + ''' + return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) + +class BottleneckCSP(nn.Module): + ''' + 标准ottleneckCSP层 + (self, in_channels, out_channels, Bottleneck层重复次数, shortcut_flag, group, expansion隐藏神经元的缩放因子) + out_size = in_size + ''' + # 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))) # CONV + BottleNeck + Conv2d out_channels = c_ + y2 = self.cv2(x) # Conv2d out_channels = c_ + return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1)))) # concat(y1 + y2) + BN + LeakyReLU + Conv2d out_channels = c2 + +class SPP(nn.Module): + ''' + 空间金字塔池化SPP： + (self, in_channels, out_channels, 池化尺寸strides[3]) + ''' + # 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) + # 建立5×5 9×9 13×13的最大池化处理过程的list + 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 : 把宽度w和高度h的信息整合到c空间中 + (self, in_channels, out_channels, kernel_size, stride, padding, group, activation_flag) + ''' + # 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(batch_size, channels, height, width) -> y(batch_size, 4*channels, height/2, weight/2) + ''' + # ::代表[start:end:step], 以2为步长取值 + 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): + ''' + (dimension) + 默认d=1按列拼接 ， d=0则按行拼接 + ''' + # 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 = 0.3 # confidence threshold + iou = 0.6 # IoU threshold + classes = None # (optional list) filter by class + + def __init__(self, dimension=1): + 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 Flatten(nn.Module): + ''' + 在全局平均池化以后使用，去掉2个维度 + (batch_size, channels, size, size) -> (batch_size, channels*size*size) + ''' + # 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): + ''' + (self, in_channels, out_channels, kernel_size=1, stride=1, padding=None, groups=1) + (batch_size, channels, size, size) -> (batch_size, channels*1*1) + ''' + # 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__() + # 给定输入数据和输出数据的大小，自适应算法能够自动帮助我们计算核的大小和每次移动的步长 + self.aap = nn.AdaptiveAvgPool2d(1) # to x(batch_size,ch_in,1,1) 返回1×1的池化结果 + self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) # to x(batch_size,ch_out,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 x is list + return self.flat(self.conv(z)) # flatten to x(batch_size, ch_out×1×1) diff --git a/models/experimental.py b/models/experimental.py new file mode 100644 index 00000000..2e354a64 --- /dev/null +++ b/models/experimental.py @@ -0,0 +1,158 @@ +# This file contains experimental modules +# 此文件包含实验模块 + +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, g, act) + self.cv2 = Conv(c_, c_, 5, 1, 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): + ''' + return model_inference， None + ''' + # 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): + ''' + ('weights_path', device) + # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a + # 加载一组模型权重=[a、b、c]或单个模型权重=[a]或权重=a + return model + 模型前向传播输出为： + model_inference， None + ''' + model = Ensemble() + + for w in weights if isinstance(weights, list) else [weights]: + attempt_download(w) + # 以FP32数据类型载入权重文件 并将数据送到对应设备， 同时 + model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval()) # load FP32 model + + if len(model) == 1: # 如果只有单个模型权重则加载单个模型权重 + 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 diff --git a/models/export.py b/models/export.py new file mode 100644 index 00000000..c5e96f13 --- /dev/null +++ b/models/export.py @@ -0,0 +1,94 @@ +"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats + +Usage: + $ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1 +""" + +import argparse +import sys +import time + +sys.path.append('./') # to run '$ 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='./yolov5s.pt', help='weights path') # from yolov5/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)) diff --git a/models/hub/yolov3-spp.yaml b/models/hub/yolov3-spp.yaml new file mode 100644 index 00000000..b6cadd9f --- /dev/null +++ b/models/hub/yolov3-spp.yaml @@ -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) + ] diff --git a/models/hub/yolov5-fpn.yaml b/models/hub/yolov5-fpn.yaml new file mode 100644 index 00000000..4d2fae10 --- /dev/null +++ b/models/hub/yolov5-fpn.yaml @@ -0,0 +1,42 @@ +# 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 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, BottleneckCSP, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, BottleneckCSP, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 1, SPP, [1024, [5, 9, 13]]], + [-1, 6, BottleneckCSP, [1024]], # 9 + ] + +# YOLOv5 FPN head +head: + [[-1, 3, BottleneckCSP, [1024, False]], # 10 (P5/32-large) + + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Conv, [512, 1, 1]], + [-1, 3, BottleneckCSP, [512, False]], # 14 (P4/16-medium) + + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Conv, [256, 1, 1]], + [-1, 3, BottleneckCSP, [256, False]], # 18 (P3/8-small) + + [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/models/hub/yolov5-panet.yaml b/models/hub/yolov5-panet.yaml new file mode 100644 index 00000000..9ed05ddc --- /dev/null +++ b/models/hub/yolov5-panet.yaml @@ -0,0 +1,48 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [116,90, 156,198, 373,326] # P5/32 + - [30,61, 62,45, 59,119] # P4/16 + - [10,13, 16,30, 33,23] # P3/8 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, BottleneckCSP, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, BottleneckCSP, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, BottleneckCSP, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 1, SPP, [1024, [5, 9, 13]]], + [-1, 3, BottleneckCSP, [1024, False]], # 9 + ] + +# YOLOv5 PANet head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, BottleneckCSP, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P5, P4, P3) + ] diff --git a/models/yolo.py b/models/yolo.py new file mode 100644 index 00000000..241eec71 --- /dev/null +++ b/models/yolo.py @@ -0,0 +1,452 @@ +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 +from models.experimental import MixConv2d, CrossConv, C3 +from utils.general import check_anchor_order, 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) + +class Detect(nn.Module): # 定义检测网络 + ''' + input:(number_classes, anchors=(), ch=(tensor_small,tensor_medium,tensor_large)) tensor[i]:(batch_size, in_channels, size1, size2) + size1[i] = img_size1/(8*i) size2[i] = img_size2/(8*i) eg: tensor_small:(batch_size, inchannels, img_size1/8. img_size2/8) + ''' + stride = None # strides computed during build + export = False # onnx export,网络模型输出为onnx格式，可在其他深度学习框架上运行 + + def __init__(self, nc=16, anchors=(), ch=()): # detection layer + super(Detect, self).__init__() + self.nc = nc # number of classes + self.angle = 180 + self.no = nc + 5 + self.angle # number of outputs per anchor （xywh + score + num_classes + num_angle） + self.nl = len(anchors) # number of detection layers 3 三种步长的检测网络 + self.na = len(anchors[0]) // 2 # number of anchors 6//2=3 每种网络3种anchor框 + self.grid = [torch.zeros(1)] * self.nl # init grid [tensor([0.]), tensor([0.]), tensor([0.])] 初始化网格 + # anchor.shape= (3 , 6) -> shape= ( 3 , 3 , 2) + a = torch.tensor(anchors).float().view(self.nl, -1, 2) # shape(3, ?(3), 2) + # register_buffer用法：内存中定义一个常量，同时，模型保存和加载的时候可以写入和读出 + self.register_buffer('anchors', a) # shape(nl,na,2) = (3, 3, 2) + # shape(3, 3, 2) -> shape(3, 1, 3, 1, 1, 2) + self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,？(na),1,1,2) = (3, 1, 3, 1, 1, 2) + self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv + ''' + m( + (0) : nn.Conv2d(in_ch[0]（17）, (nc + 5 + self.angle) * na, kernel_size=1) # 每个锚框中心点有3种尺度的anchor，每个anchor有 no 个输出 + (1) : nn.Conv2d(in_ch[1]（20）, (nc + 5 + self.angle) * na, kernel_size=1) + (2) : nn.Conv2d(in_ch[2]（23）, (nc + 5 + self.angle) * na, kernel_size=1) + ) + ''' + + def forward(self, x): + ''' + 相当于最后生成的feature map分辨率为size1 × size2.即映射到原图，有size1 × size2个锚点，以锚点为中心生成锚框来获取Region proposals，每个锚点代表一个[xywh,score,num_classes]向量 + forward(in_tensor) in_tensor:[(P3/8-small), (P4/16-medium), (P5/32-large)] (3种size的featuremap, batch_size, no * na , size_1, size2) + return : + if training : x list: [small_forward, medium_forward, large_forward] eg:small_forward.size=( batch_size, 3种scale框, size1, size2, [xywh,score,num_classes,num_angle]) + else : (z,x) + z tensor: [small+medium+large_inference] size=(batch_size, 3 * (small_size1*small_size2 + medium_size1*medium_size2 + large_size1*large_size2), no) 真实坐标 + x list: [small_forward, medium_forward, large_forward] eg:small_forward.size=( batch_size, 3种scale框, size1, size2, [xywh,score,num_classes,num_angle]) + ''' + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + for i in range(self.nl): # nl = 3 in:(batch_size, no * na, size1, size2) + # x[i].shape(batch_size , (5+nc+180) * na, size1/8*(i+1) , size2/8*(i+1)) + x[i] = self.m[i](x[i]) # conv yolo_out[i] 对各size的feature map分别进行head检测 small medium large + # ny为featuremap的height， nx为featuremap的width + bs, _, ny, nx = x[i].shape # x[i]:(batch_size, (5+nc+180) * na, size1', size2') + + # x(batch_size,(5+nc+180) * 3,size1',size2') to x(batch_size,3种框,(5+nc+180),size1',size2') + # x(batch_size,3种框,(5+nc+180),size1',size2') to x(batch_size, 3种框, size1', size2', (5+nc+180)) + x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + + if not self.training: # inference推理模式 + # grid[i].shape[2:4]=[size1, size2] 即[height/8*i, width/8*i] 与对应的featuremap层尺度一致 + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + # grid[i]: tensor.shape(1, 1,当前featuremap的height, 当前featuremap的width, 2) + # 以height为y轴，width为x轴的grid坐标 坐标按顺序（0, 0） （1, 0）... (width-1, 0) (0, 1) (1,1) ... (width-1, 1) ... (width-1 , height-1) + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + + # y:(batch_size,3种scale框,size1,size2,[xywh,score,num_classes,num_angle]) + y = x[i].sigmoid() + # i : 0为small_forward 1为medium_forward 2为large_forward + # self.grid[i]: tensor.shape(1, 1,当前featuremap的height, 当前featuremap的width, 2) 以height为y轴，width为x轴的grid坐标 + # grid坐标按顺序（0, 0） （1, 0）... (width-1, 0) (0, 1) (1,1) ... (width-1, 1) ... (width-1 , height-1) + # self.stride = ([ 8., 16., 32.]) + # self.anchor_grid: shape(3, 1, 3, 1, 1, 2) + y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy 预测的真实坐标 y[..., 0:2] * 2. - 0.5是相对于左上角网格的偏移量； self.grid[i]是网格坐标索引 + # anchor_grid[i].shape=(1, 3, 1, 1, 2) y[..., 2:4].shape=(bs, 3, height', width', 2) + y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh 预测的真实wh self.anchor_grid[i]是原始anchors宽高 (y[..., 2:4] * 2) ** 2 是预测出的anchors的wh倍率 + z.append(y.view(bs, -1, self.no)) # z:(batch_size, 累加3*size1*size2 , (5+nc+180)) z会一直在[1]维度上增添数据 + + return x if self.training else (torch.cat(z, 1), x) + + @staticmethod + def _make_grid(nx=20, ny=20): # 绘制网格 + """ + 绘制网格 eg：640 × 480的图像在detect层第一层中featuremap大小为 80 × 60，此时要生成 80 × 60的网格在原图上 + @param nx: 当前featuremap的width + @param ny: 当前featuremap的height + @return: tensor.shape(1, 1, 当前featuremap的height, 当前featuremap的width, 2) 生成以height为y轴，width为x轴的grid坐标 + 坐标按顺序（0, 0） （1, 0）... (width-1, 0) (0, 1) (1,1) ... (width-1, 1) ... (width-1 , height-1) + """ + # 初始化ny行 × nx列的tensor + ''' + eg: 初始化ny=80行 × nx=64列的tensor + yv = tensor([[ 0, 0, 0, ..., 0, 0, 0], xv = tensor([[ 0, 1, 2, ..., 61, 62, 63], + [ 1, 1, 1, ..., 1, 1, 1], [ 0, 1, 2, ..., 61, 62, 63], + ..., ..., + [79, 79, 79, ..., 79, 79, 79]]) [ 0, 1, 2, ..., 61, 62, 63]]) + ''' + yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + # 将两个 ny×ny 和 nx×nx的tensor在dim=2的维度上进行堆叠 shape(ny, nx, 2) + ''' + eg: tensor([[ + [ 0, 0], [[ 0, 1], [[ 0, 2], [[ 0, 79], + [ 1, 0], [ 1, 1], [ 1, 2], [ 1, 79], + ..., ..., ..., ..., ..., + [63, 0]], [63, 1]], [63, 2]], [63, 79] + ]]) + ''' + # tensor.shape(ny, nx, 2) -> shape(1, 1, ny, nx, 2) + return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + +class Model(nn.Module): + ''' + 构建成员变量self.stride = ([ 8., 16., 32.]) ； + 更改Detect类的成员变量anchors; anchor.shape(3, 3, 2) anchors: -> anchor(0,:,:)/ 8. , anchor(1,:,:)/ 16. anchor(2,:,:)/ 32. + Model (model, cfg_file, in_channnels, num_classes) + model = Sequential( + (0): Focus(...) + ...... + (24):Detect(...) + ) + ''' + def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None): # model, input channels, number of classes + super(Model, self).__init__() + if isinstance(cfg, dict): # 有预训练权重文件时cfg加载权重中保存的cfg字典内容； + self.yaml = cfg # model dict + else: # is *.yaml 没有预训练权重文件时加载用户定义的opt.cfg权重文件路径，再载入文件中的内容到字典中 + 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']: # 字典中的nc与data.yaml中的nc不同，则以data.yaml中的nc为准 + print('Overriding model.yaml nc=%g with nc=%g' % (self.yaml['nc'], nc)) + self.yaml['nc'] = nc # override yaml value + # 返回（网络模型, Detect和Concat需要使用到的网络层数参数信息） + # return： 网络模型每层的结构名序列：(nn.Sequential(*layers), [6, 4, 14, 10, 17, 20, 23]) + 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() 模型的最后一个函数为Detect层 + if isinstance(m, Detect): + s = 128 # 2x min stride + # 此时 x.shape = (1, 3, s/8或16或32, 5+nc) 所以 x.shape[-2]=[s/8, s/16, s/32] + # tensor: stride = ([ 8., 16., 32.]) + m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward + # 先将stride维度提升到(3, 1, 1) 之后进行每个维度的数据处理，使得 detect类的成员变量anchors由原图的尺度对应到最终的featuremaps尺度 + # anchor(3, 3, 2) anchors: -> anchor(0,:,:)/ 8. , anchor(1,:,:)/ 16. anchor(2,:,:)/ 32. + m.anchors /= m.stride.view(-1, 1, 1) + check_anchor_order(m) # 确保anchors的元素顺序是从小物体的anchor到大物体的anchor + # self.stride = ([ 8., 16., 32.]) + self.stride = m.stride + self._initialize_biases() # only run once + # print('Strides: %s' % m.stride.tolist()) + + # Init weights, biases + initialize_weights(self) + self.info() + print('') + + def forward(self, x, augment=False, profile=False): + ''' + 该函数为前向计算函数，输入向量经函数计算后，返回backbone+head+detect计算结果 + @param x: in_tensor shape(batch_size, 3, height, width)预处理后的图像 + @param augment: 默认为False + @param profile: 是否估计Pytorch模型的FLOPs的标志位 + @return: + if augment: (图像增强后的推理结果 , None) + else: (整体网络模型backbone+head+detect前向计算结果): + if training : x list: [small_forward, medium_forward, large_forward] eg:small_forward.size=( batch_size, 3种scale框, size1, size2, [xywh,score,num_classes,num_angle]) + else : (z,x) + tensor: [small+medium+large_inference] size=(batch_size, 3 * (small_size1*small_size2 + medium_size1*medium_size2 + large_size1*large_size2), (5+nc+180)) + x list: [small_forward, medium_forward, large_forward] eg:small_forward.size=( batch_size, 3种scale框, size1, size2, [xywh,score,num_classes,num_angle]) + -> + if profile=True： return out_tensor + ''' + 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): + ''' + 该函数为前向计算函数，输入向量经函数计算后，返回backbone+head+detect计算结果 + @param x: 待前向传播的向量 size=(batch_size, 3, height, width) + @param profile: 是否估计Pytorch模型的FLOPs的标志位 + @return: (整体网络模型backbone+head+detect前向计算结果): + if training : x list: [small_forward, medium_forward, large_forward] eg:small_forward.size=( batch_size, 3种scale框, size1, size2, [xywh,score,num_classes,num_angle]) + else : (z,x) + z tensor: [small+medium+large_inference] size=(batch_size, 3 * (small_size1*small_size2 + medium_size1*medium_size2 + large_size1*large_size2), no) + x list: [small_forward, medium_forward, large_forward] eg:small_forward.size=( batch_size, 3种scale框, size1, size2, [xywh,score,num_classes,num_angle]) + ''' + y, dt = [], [] # outputs + for m in self.model: + # parser_model函数中定义的成员变量 m_.f, m_.type, m_.np = f, t, np # 'from' index, module层名（如Detect Focus）, module对应层中的参数数量 + if m.f != -1: # from : if not from previous layer / if current layer is concat or SPP + # x为待concat/Detect的层网络的前向计算结果 + # 例子：m=Concat层函数 m.f = [-1, 4], x = [x,y[4]] ,即x= [上一层的前向计算结果, 第四层的前向计算结果] + # y list：需要Concat/Detect的层数前向计算的结果 y = [None,None,None,None,第四层的前向计算结果,None,第六层的前向计算结果....] + 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: + try: + import thop + o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # FLOPS + except: + o = 0 + 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 ，前向计算网络每层；m不为concat/Detect时直接前向计算，否则先更改x为待计算的对应层数的前向计算结果，再进行Concat/Detect + # m.i = 0/1/2/3/...../24; m.i表示当前第几个标准函数层 + # 把需要Concat/Detect的层数前向计算结果保存在y list中 + # 例：self.save=[6, 4, 14, 10, 17, 20, 23] ;y = [None,None,None,None,第四层的前向计算结果,None,第六层的前向计算结果......] + 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 + # 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 ，融合该两层模型 + 在网络的推理阶段，可以将BN层的运算融合到Conv层中，减少运算量，加速推理 + ''' + print('Fusing layers... ') + ''' + type(m) = + + + + + + ... + ''' + for m in self.model.modules(): + if type(m) is Conv and hasattr(m, 'bn'): # 如果函数层名为Conv标准卷积层，且同时 层中包含‘bn’属性名 + m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatability + m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv + delattr(m, 'bn') # remove batchnorm 将'bn'属性删除 + m.forward = m.fuseforward # update forward + self.info() + return self + + def add_nms(self): # fuse model Conv2d() + BatchNorm2d() layers + if type(self.model[-1]) is not NMS: # if missing NMS + print('Adding NMS module... ') + 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 + return self + + def info(self, verbose=False): # print model information + model_info(self, verbose) + + +def parse_model(d, ch): # model_dict, input_channels(3) + ''' + @param d: cfg_file/model_dict; + @param ch: 3 + @return: (nn.Sequential(*layers), [6, 4, 14, 10, 17, 20, 23]) (网络， Concat和Detect需要使用到的网络层索引信息) + ''' + + logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) # 打印相关参数的类名 + 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 6//2=3 + no = na * (nc + 5) # number of outputs = anchors * (classes + 5) = 3*85 =255 + + layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out [] [] 3 + ''' + 从yaml文件中读取模型网络结构参数 + from : -1 代表是从上一层获得的输入; -2表示从上两层获得的输入（head同理） + number : module重复的次数 + module : 功能模块 common.py中定义的函数 + args : 功能函数的输入参数定义 + ''' + for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args + # 若module参数为字符串，则直接执行表达式 + m = eval(m) if isinstance(m, str) else m # eval strings + for j, a in enumerate(args): + try: + # 若arg参数为字符串，则直接执行表达式(如Flase None等)，否则直接等于数字本身（如64，128等） + args[j] = eval(a) if isinstance(a, str) else a # eval strings + except: + pass + # 模块重复次数为1时 ：n为1， 否则 ： n= （n * gd）向上取整 + n = max(round(n * gd), 1) if n > 1 else n # depth gain,BottleneckCSP层中Bottleneck层的个数 + + # 排除concat，Unsample，Detect的情况 + if m in [Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]: + # ch每次循环都会扩增[3]-> [3,80] -> [3,80,160] -> [3,80,160,160] -> ''' + c1, c2 = ch[f], args[0] # c1 = 3， c2 = 每次module函数中的out_channels参数 + + # 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: + + ''' + 若c2不等于85（num_classes + 5）则 ： c2=make_divisible(c2 * gw, 8)确保能把8整除 ； 否则：c2=c2 + ''' + 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:]] # [ch[-1], out_channels, kernel_size, strides(可能)] — 除了BottleneckCSP与C3层 + if m in [BottleneckCSP, C3]: + args.insert(2, n) # [ch[-1], out_channnels, Bottleneck_num] — BottleneckCSP与C3层 + n = 1 + + elif m is nn.BatchNorm2d: + args = [ch[f]] + elif m is Concat: + # 以第一个concat为例 ： ch[-1] + ch[x+1] = ch[-1]+ch[7] = 640 + 640 = 1280 + 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] + + # 构建n次的module处理模块，如构建 4次 BottleneckCSP层的模块，输入参数由args导入 + '''以第一层focus为例 + args： [ch[-1], out_channels, kernel_size, strides(可能)] = [3, 80, 3] + m: class 'models.common.Focus' + m_: Focus( # focus函数会在一开始将3通道的图像再次分为12通道 + (conv): Conv( + (conv): Conv2d(12, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (act): Hardswish() + ) + ) + ''' + m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module + # 将'__main__.Detect'变为Detect，其余模块名不变，相当于所有函数名全都放在了t中 + t = str(m)[8:-2].replace('__main__.', '') # module type + # 返回当前module结构中参数的总数目 + 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 + # 对应相关参数的类名，打印对应参数 + logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print + # 把Concat，Detect需要使用到的参数层的层数信息储存进save + save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist + # 将每层结构的函数名拓展进layers list + layers.append(m_) + # 将每层结构的out_channels拓展进ch，以便下一层结构调用上一层的输出通道数 yolov5.yaml中的第0层的输出对应ch[1] ;i - ch[i+1] + ch.append(c2) + ''' + layers=[ + Focus(...) + Conv(...) + ... + Detect(...) + ] + + ''' + return nn.Sequential(*layers), sorted(save) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--cfg', type=str, default='yolov5x.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) + + # ONNX export + # model.model[-1].export = True + # torch.onnx.export(model, img, opt.cfg.replace('.yaml', '.onnx'), verbose=True, opset_version=11) + + # 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 diff --git a/models/yolov5l.yaml b/models/yolov5l.yaml new file mode 100644 index 00000000..13095541 --- /dev/null +++ b/models/yolov5l.yaml @@ -0,0 +1,48 @@ +# 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 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, BottleneckCSP, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, BottleneckCSP, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, BottleneckCSP, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 1, SPP, [1024, [5, 9, 13]]], + [-1, 3, BottleneckCSP, [1024, False]], # 9 + ] + +# YOLOv5 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, BottleneckCSP, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/models/yolov5m.yaml b/models/yolov5m.yaml new file mode 100644 index 00000000..76f17509 --- /dev/null +++ b/models/yolov5m.yaml @@ -0,0 +1,48 @@ +# parameters +nc: 16 # number of classes +depth_multiple: 0.67 # model depth multiple +width_multiple: 0.75 # layer channel multiple + +# anchors coco:24,9, 37,12, 52,15 64,23, 81,19, 98,29 137,27, 199,41, 342,65 +anchors: + - [10,5, 21,10, 38,16] # P3/8 + - [65,23, 75,53, 167,33] # P4/16 + - [178,93, 493,80, 486,271] # P5/32 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, BottleneckCSP, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, BottleneckCSP, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, BottleneckCSP, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 1, SPP, [1024, [5, 9, 13]]], + [-1, 3, BottleneckCSP, [1024, False]], # 9 + ] + +# YOLOv5 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, BottleneckCSP, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/models/yolov5s.yaml b/models/yolov5s.yaml new file mode 100644 index 00000000..2bec4529 --- /dev/null +++ b/models/yolov5s.yaml @@ -0,0 +1,48 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # 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 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, BottleneckCSP, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, BottleneckCSP, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, BottleneckCSP, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 1, SPP, [1024, [5, 9, 13]]], + [-1, 3, BottleneckCSP, [1024, False]], # 9 + ] + +# YOLOv5 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, BottleneckCSP, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/models/yolov5x.yaml b/models/yolov5x.yaml new file mode 100644 index 00000000..a82c2ee6 --- /dev/null +++ b/models/yolov5x.yaml @@ -0,0 +1,52 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.33 # model depth multiple Control Bottleneck numbers in BottleneckCSP layer +width_multiple: 1.25 # layer channel multiple Control kernels number + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 w,h + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + # from : -1 - front layers output -2 - front front layers output + # number : module repeat number + # module : function module in common.py + # args : input parameter of function module + [[-1, 1, Focus, [64, 3]], # 0-P1/2 Focus(3, 64, 1, 1) + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 Conv(-1(64) , 128, 3, 2) + [-1, 3, BottleneckCSP, [128]], # BottleneckCSP(-1(128), 128)×3 + [-1, 1, Conv, [256, 3, 2]], # Conv(-1(128) , 256, 3, 2) + [-1, 9, BottleneckCSP, [256]], # 4-P3/8 BottleneckCSP(-1(256), 256)×9 + [-1, 1, Conv, [512, 3, 2]], # Conv(-1(256) , 512, 3, 2) + [-1, 9, BottleneckCSP, [512]], # 6-P4/16 BottleneckCSP(-1(512), 512)×9 + [-1, 1, Conv, [1024, 3, 2]], # Conv(-1(512) , 1024, 3, 2) + [-1, 1, SPP, [1024, [5, 9, 13]]], # 8 SPP(-1(1024), 1024, [5,9,13]) + [-1, 3, BottleneckCSP, [1024, False]], # 9-P5/32 BottleneckCSP(-1(1024), 1024, shorcut=False)×3 + ] + +# YOLOv5 head torch.nn.Upsample(in_size=None, scale_factor=None, mode='nearest', align_corners=None) +head: + [[-1, 1, Conv, [512, 1, 1]], # Conv(-1(1024) , 512, 1, 1) out_size = 1/32 10-head P5 + [-1, 1, nn.Upsample, [None, 2, 'nearest']], # Upsample(-1(1/32), 2) out_size = 1/16 + [[-1, 6], 1, Concat, [1]], # cat backbone P4 out_channels = 512 + 512 = 1024 out_size = 1/16 + [-1, 3, BottleneckCSP, [512, False]], # 13 BottleneckCSP(-1(1024), 512)×3 out_size = 1/16 + + [-1, 1, Conv, [256, 1, 1]], # Conv(-1(512) , 256, 1, 1) out_size = 1/16 14-head P4 + [-1, 1, nn.Upsample, [None, 2, 'nearest']], # Upsample(-1(1/16), 2) out_size = 1/8 + [[-1, 4], 1, Concat, [1]], # cat backbone P3 out_channels = 256 + 256 = 512 out_size = 1/8 + [-1, 3, BottleneckCSP, [256, False]], # 17 BottleneckCSP(-1(512), 256)×3 out_size = 1/8 17-(P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], # Conv(-1(256) , 256, 3, 2) out_size = 1/16 + [[-1, 14], 1, Concat, [1]], # cat head P4 out_channels = 256 + 256 = 512 out_size = 1/16 + [-1, 3, BottleneckCSP, [512, False]], # 20 BottleneckCSP(-1(512), 512)×3 out_size = 1/16 20-(P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], # Conv(-1(512) , 512, 3, 2) out_size = 1/32 + [[-1, 10], 1, Concat, [1]], # cat head P5 out_channels = 512 + 512 = 512 out_size = 1/32 + [-1, 3, BottleneckCSP, [1024, False]], # 23 BottleneckCSP(-1(512), 1024)×3 out_size = 1/32 23-(P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..0871ed66 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,27 @@ +# pip install -r requirements.txt + +# base ---------------------------------------- +Cython +matplotlib>=3.2.2 +numpy>=1.18.5 +opencv-python>=4.1.2 +pillow +PyYAML>=5.3 +scipy>=1.4.1 +tensorboard>=2.2 +torch>=1.6.0 +torchvision>=0.7.0 +tqdm>=4.41.0 + +# coco ---------------------------------------- +# pycocotools>=2.0 + +# export -------------------------------------- +# packaging # for coremltools +# coremltools==4.0 +# onnx>=1.7.0 +# scikit-learn==0.19.2 # for coreml quantization + +# extras -------------------------------------- +# thop # FLOPS computation +# seaborn # plotting diff --git a/sotabench.py b/sotabench.py new file mode 100644 index 00000000..96ea6bff --- /dev/null +++ b/sotabench.py @@ -0,0 +1,310 @@ +import argparse +import glob +import json +import os +import shutil +from pathlib import Path + +import numpy as np +import torch +import yaml +from tqdm import tqdm + +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, compute_loss, non_max_suppression, scale_coords, + xyxy2xywh, clip_coords, plot_images, xywh2xyxy, box_iou, output_to_target, ap_per_class, set_logging) +from utils.torch_utils import select_device, time_synchronized + + +from sotabencheval.object_detection import COCOEvaluator +from sotabencheval.utils import is_server + +DATA_ROOT = './.data/vision/coco' if is_server() else '../coco' # sotabench data dir + + +def test(data, + weights=None, + batch_size=16, + imgsz=640, + conf_thres=0.001, + iou_thres=0.6, # for NMS + save_json=False, + single_cls=False, + augment=False, + verbose=False, + model=None, + dataloader=None, + save_dir='', + merge=False, + save_txt=False): + # Initialize/load model and set device + training = model is not None + if training: # called by train.py + device = next(model.parameters()).device # get model device + + else: # called directly + set_logging() + device = select_device(opt.device, batch_size=batch_size) + merge, save_txt = opt.merge, opt.save_txt # use Merge NMS, save *.txt labels + if save_txt: + out = Path('inference/output') + if os.path.exists(out): + shutil.rmtree(out) # delete output folder + os.makedirs(out) # make new output folder + + # Remove previous + for f in glob.glob(str(Path(save_dir) / 'test_batch*.jpg')): + os.remove(f) + + # 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() + 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 + niou = iouv.numel() + + # Dataloader + 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, + hyp=None, augment=False, cache=True, pad=0.5, rect=True)[0] + + seen = 0 + names = model.names if hasattr(model, 'names') else model.module.names + coco91class = coco80_to_coco91_class() + 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 = [], [], [], [] + evaluator = COCOEvaluator(root=DATA_ROOT, model_name=opt.weights.replace('.pt', '')) + 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 = img.shape # batch size, channels, height, width + whwh = torch.Tensor([width, height, width, height]).to(device) + + # Disable gradients + with torch.no_grad(): + # Run model + t = time_synchronized() + inf_out, train_out = model(img, augment=augment) # inference and training outputs + t0 += time_synchronized() - t + + # Compute loss + if training: # if model has loss hyperparameters + loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3] # box, obj, cls + + # Run NMS + t = time_synchronized() + output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, merge=merge) + 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 + seen += 1 + + if pred is None: + if nl: + stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls)) + continue + + # Append to text file + if save_txt: + gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]] # normalization gain whwh + x = pred.clone() + x[:, :4] = scale_coords(img[si].shape[1:], x[:, :4], shapes[si][0], shapes[si][1]) # to original + for *xyxy, conf, cls in x: + xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + with open(str(out / Path(paths[si]).stem) + '.txt', 'a') as f: + f.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format + + # Clip boxes to image bounds + clip_coords(pred, (height, width)) + + # 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 = Path(paths[si]).stem + box = pred[:, :4].clone() # xyxy + scale_coords(img[si].shape[1:], box, shapes[si][0], shapes[si][1]) # to original shape + box = xyxy2xywh(box) # xywh + box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner + for p, b in zip(pred.tolist(), box.tolist()): + result = {'image_id': int(image_id) if image_id.isnumeric() else image_id, + 'category_id': coco91class[int(p[5])], + 'bbox': [round(x, 3) for x in b], + 'score': round(p[4], 5)} + jdict.append(result) + + #evaluator.add([result]) + #if evaluator.cache_exists: + # break + + # # Assign all predictions as incorrect + # correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device) + # if nl: + # detected = [] # target indices + # tcls_tensor = labels[:, 0] + # + # # target boxes + # tbox = xywh2xyxy(labels[:, 1:5]) * whwh + # + # # Per target class + # for cls in torch.unique(tcls_tensor): + # 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 + # + # # Append detections + # detected_set = set() + # for j in (ious > iouv[0]).nonzero(as_tuple=False): + # d = ti[i[j]] # detected target + # 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 + # break + # + # # Append statistics (correct, conf, pcls, tcls) + # stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls)) + + # # Plot images + # if batch_i < 1: + # f = Path(save_dir) / ('test_batch%g_gt.jpg' % batch_i) # filename + # plot_images(img, targets, paths, str(f), names) # ground truth + # f = Path(save_dir) / ('test_batch%g_pred.jpg' % batch_i) + # plot_images(img, output_to_target(output, width, height), paths, str(f), names) # predictions + + evaluator.add(jdict) + evaluator.save() + + # # Compute statistics + # stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy + # if len(stats) and stats[0].any(): + # p, r, ap, f1, ap_class = ap_per_class(*stats) + # 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) + # + # # Print results + # 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], ap50[i], ap[i])) + # + # # Print speeds + # 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 len(jdict): + # f = 'detections_val2017_%s_results.json' % \ + # (weights.split(os.sep)[-1].replace('.pt', '') if isinstance(weights, str) else '') # filename + # print('\nCOCO mAP with pycocotools... saving %s...' % f) + # with open(f, 'w') as file: + # json.dump(jdict, file) + # + # try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb + # from pycocotools.coco import COCO + # from pycocotools.cocoeval import COCOeval + # + # imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files] + # cocoGt = COCO(glob.glob('../coco/annotations/instances_val*.json')[0]) # initialize COCO ground truth api + # cocoDt = cocoGt.loadRes(f) # initialize COCO pred api + # cocoEval = COCOeval(cocoGt, cocoDt, 'bbox') + # cocoEval.params.imgIds = imgIds # image IDs to evaluate + # cocoEval.evaluate() + # cocoEval.accumulate() + # cocoEval.summarize() + # map, map50 = cocoEval.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 + # model.float() # for training + # maps = np.zeros(nc) + map + # for i, c in enumerate(ap_class): + # maps[c] = ap[i] + # return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(prog='test.py') + parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)') + parser.add_argument('--data', type=str, default='data/coco.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=0.65, 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='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('--merge', action='store_true', help='use Merge NMS') + 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') + opt = parser.parse_args() + opt.save_json |= opt.data.endswith('coco.yaml') + opt.data = check_file(opt.data) # check file + print(opt) + + if opt.task in ['val', 'test']: # run normally + test(opt.data, + opt.weights, + opt.batch_size, + opt.img_size, + opt.conf_thres, + opt.iou_thres, + opt.save_json, + opt.single_cls, + opt.augment, + opt.verbose) + + elif opt.task == 'study': # run over a range of settings and save/plot + for weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.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) + y.append(r + t) # results and times + np.savetxt(f, y, fmt='%10.4g') # save + os.system('zip -r study.zip study_*.txt') + # utils.general.plot_study_txt(f, x) # plot \ No newline at end of file diff --git a/test.py b/test.py new file mode 100644 index 00000000..04c0e2c2 --- /dev/null +++ b/test.py @@ -0,0 +1,368 @@ +import argparse +import glob +import json +import os +import shutil +from pathlib import Path + +import numpy as np +import torch +import yaml +from tqdm import tqdm + +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, compute_loss, rotate_non_max_suppression, + xyxy2xywh, plot_images, xywh2xyxy, box_iou, output_to_target, ap_per_class, set_logging) +from utils.torch_utils import select_device, time_synchronized + + +def test(data, + weights=None, + batch_size=16, + imgsz=640, + conf_thres=0.001, + iou_thres=0.6, # for NMS + save_json=False, + single_cls=False, + augment=False, + verbose=False, + model=None, + dataloader=None, + save_dir=Path(''), # for saving images + save_txt=False, # for auto-labelling + plots=True): + + # Initialize/load model and set device + # 判断是否在训练时调用test，如果是则获取训练时的设备 + training = model is not None + if training: # called by train.py + device = next(model.parameters()).device # get model device + + else: # called directly + set_logging() + device = select_device(opt.device, batch_size=batch_size) + save_txt = opt.save_txt # save *.txt labels + if save_txt: + out = Path('inference/output') + if os.path.exists(out): + shutil.rmtree(out) # delete output folder + os.makedirs(out) # make new output folder + + # Remove previous + # 删除之前的test_batch0_gt.jpg和test_batch0_pred.jpg + for f in glob.glob(str(save_dir / 'test_batch*.jpg')): + os.remove(f) + + # 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 + # 如果设备不是cpu，则将模型由Float32转为Float16，提高前向传播的速度 + half = device.type != 'cpu' # half precision only supported on CUDA + if half: + model.half() + + # Configure + # 将模型字符串转变为函数 + model.eval() + 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 + # 设置iou阈值，从0.5~0.95，每间隔0.05取一次 + iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95 + # iou个数 + niou = iouv.numel() + + # Dataloader + if not training: + # 创建一个全0数组测试一下前向传播是否正常运行 + 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 + + # 获取图片路径 + path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images + # 创建dataloader + # 注意这里rect参数为True，yolov5的测试评估是基于矩形推理的 + dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, + hyp=None, augment=False, cache=False, pad=0.5, rect=True)[0] + + # 初始化测试的图片数量 + seen = 0 + # 获取类别的名字 + names = model.names if hasattr(model, 'names') else model.module.names + """ + 获取coco数据集的类别索引 + 这里要说明一下，coco数据集有80个类别(索引范围应该为0~79)， + 但是他的索引却属于0~90(笔者是通过查看coco数据测试集的json文件发现的，具体原因不知) + coco80_to_coco91_class()就是为了与上述索引对应起来，返回一个范围在0~90的索引数组 + """ + coco91class = coco80_to_coco91_class() + # 设置tqdm进度条的显示信息 + s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95') + # 初始化指标，时间 + p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0. + # 初始化测试集的损失 + loss = torch.zeros(4, device=device) + # 初始化json文件的字典，统计信息，ap + jdict, stats, ap, ap_class = [], [], [], [] + for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)): + ''' + i: batch_index, 第i个batch + imgs : torch.Size([batch_size, 3, weights, heights]) + targets : torch.Size = (该batch中的目标数量, [该image属于该batch的第几个图片, class, xywh, Θ]) + paths : List['img1_path','img2_path',......,'img-1_path'] len(paths)=batch_size + shape : + ''' + img = img.to(device, non_blocking=True) + # 图片也由Float32->Float16 + 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 = img.shape # batch size, channels, height, width + whwh = torch.Tensor([width, height, width, height]).to(device) + + # Disable gradients + with torch.no_grad(): + # Run model + t = time_synchronized() + ''' + Detect层在的输出：(z,x) + if training : + x list: [small_forward, medium_forward, large_forward] eg:small_forward.size=( batch_size, 3种scale框, size1, size2, no) + else : + (z,x) + z tensor: [small+medium+large_inference] size=(batch_size, 3 * (small_size1*small_size2 + medium_size1*medium_size2 + large_size1*large_size2), no) 真实坐标 + x list: [small_forward, medium_forward, large_forward] eg:small_forward.size=( batch_size, 3种scale框, size1, size2, no) + ''' + inf_out, train_out = model(img, augment=augment) # inference and training outputs + t0 += time_synchronized() - t + + # Compute loss + if training: # if model has loss hyperparameters + loss += compute_loss([x.float() for x in train_out], targets, model)[1][:4] # box, obj, cls, angle + + # Run NMS + t = time_synchronized() + # output : size = (batch_size, num_conf_nms, [xywhθ,conf,classid]) θ∈[0,179] + #output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres) + output = rotate_non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres) + t1 += time_synchronized() - t + + # Statistics per image + for si, pred in enumerate(output): + ''' + targets : torch.Size = (该batch中的目标数量, [该image属于该batch的第几个图片, class, xywh, θ]) θ∈[0,179] + pred : shape=(num_conf_nms, [xywhθ,conf,classid]) θ∈[0,179] + si : 该batch中的第几张图 + ''' + # labels: shape= (num, [class, xywh, θ]) + labels = targets[targets[:, 0] == si, 1:] + nl = len(labels) + tcls = labels[:, 0].tolist() if nl else [] # target class + seen += 1 + + if pred is None: + if nl: + stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls)) + continue + + # # Append to text file + # if save_txt: + # gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]] # normalization gain whwh + # x = pred.clone() + # x[:, :4] = scale_coords(img[si].shape[1:], x[:, :4], shapes[si][0], shapes[si][1]) # to original + # for *xyxy, conf, cls in x: + # xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + # with open(str(out / Path(paths[si]).stem) + '.txt', 'a') as f: + # f.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format + + # Clip boxes to image bounds + # clip_coords(pred, (height, width)) + + # 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 = Path(paths[si]).stem + # box = pred[:, :4].clone() # xyxy + # scale_coords(img[si].shape[1:], box, shapes[si][0], shapes[si][1]) # to original shape + # box = xyxy2xywh(box) # 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': int(image_id) if image_id.isnumeric() else image_id, + # 'category_id': coco91class[int(p[5])], + # 'bbox': [round(x, 3) for x in b], + # 'score': round(p[4], 5)}) + + # Assign all predictions as incorrect + correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device) + # pred : shape=(num_conf_nms, [xywhθ,conf,classid]) θ∈[0,179] + # labels: shape= (num, [class, xywh, θ]) + if nl: + detected = [] # target indices + tcls_tensor = labels[:, 0] # torch.size(num) + + # target boxes -> orignal shape + tbox = labels[:, 1:5] * whwh # torch.size(num,[xywh]) 1024*1024 无所谓顺序 + #ttheta = labels[:, 5] # torch.size(num,[Θ]) + + # Per target class + for cls in torch.unique(tcls_tensor): # unique函数去除其中重复的元素，并按元素（类别）由大到小返回一个新的无元素重复的元组或者列表 + ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # target indices + pi = (cls == pred[:, 6]).nonzero(as_tuple=False).view(-1) # prediction 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 + #rious, i = rbox_iou(pred[:, :4], pred[:, 4].unsqueeze(1), tbox, ttheta.unsqueeze(1)).max(1) # best rious, indices + + + # Append detections + detected_set = set() + for j in (ious > iouv[0]).nonzero(as_tuple=False): + d = ti[i[j]] # detected target + 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 + break + + # Append statistics (correct, conf, pcls, tcls) + stats.append((correct.cpu(), pred[:, 5].cpu(), pred[:, 6].cpu(), tcls)) + + # Plot images + if plots and batch_i < 1: + f = save_dir / ('test_batch%g_gt.jpg' % batch_i) # filename + plot_images(img, targets, paths, str(f), names) # ground truth + f = save_dir / ('test_batch%g_pred.jpg' % batch_i) + plot_images(img, output_to_target(output, width, height), paths, str(f), names) # predictions + + # Compute statistics + stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy + if len(stats) and stats[0].any(): + p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, fname=save_dir / 'precision-recall_curve.png') + 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) + + # Print results + 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], ap50[i], ap[i])) + + # Print speeds + 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 len(jdict): + f = 'detections_val2017_%s_results.json' % \ + (weights.split(os.sep)[-1].replace('.pt', '') if isinstance(weights, str) else '') # filename + print('\nCOCO mAP with pycocotools... saving %s...' % f) + with open(f, 'w') as file: + json.dump(jdict, file) + + try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb + from pycocotools.coco import COCO + from pycocotools.cocoeval import COCOeval + + imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files] + cocoGt = COCO(glob.glob('../coco/annotations/instances_val*.json')[0]) # initialize COCO ground truth api + cocoDt = cocoGt.loadRes(f) # initialize COCO pred api + cocoEval = COCOeval(cocoGt, cocoDt, 'bbox') + cocoEval.params.imgIds = imgIds # image IDs to evaluate + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + map, map50 = cocoEval.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 + model.float() # for training + maps = np.zeros(nc) + map + for i, c in enumerate(ap_class): + maps[c] = ap[i] + return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t + + +if __name__ == '__main__': + """ + opt参数详解 + weights:测试的模型权重文件 + data:数据集配置文件，数据集路径，类名等 + batch-size:前向传播时的批次, 默认32 + img-size:输入图片分辨率大小, 默认640 + conf-thres:筛选框的时候的置信度阈值, 默认0.001 + iou-thres:进行NMS的时候的IOU阈值, 默认0.65 + save-json:是否按照coco的json格式保存预测框，并且使用cocoapi做评估(需要同样coco的json格式的标签), 默认False + task:设置测试形式, 默认val, 具体可看下面代码解析注释 + device:测试的设备，cpu；0(表示一个gpu设备cuda:0)；0,1,2,3(多个gpu设备) + single-cls:数据集是否只有一个类别，默认False + augment:测试时是否使用TTA(test time augmentation), 默认False + merge:在进行NMS时，是否通过合并方式获得预测框, 默认False + verbose:是否打印出每个类别的mAP, 默认False + save-txt:是否以txt文件的形式保存模型预测的框坐标, 默认False + """ + parser = argparse.ArgumentParser(prog='test.py') + parser.add_argument('--weights', nargs='+', type=str, default='../rotation-yolov5/runs/rotated_trainDOTA_0/weights/last.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=8, help='size of each image batch') + parser.add_argument('--img-size', type=int, default=1024, 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=0.65, 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='val', help="'val', 'test', 'study'") + parser.add_argument('--device', default='2,3', 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') + opt = parser.parse_args() + opt.save_json |= opt.data.endswith('coco.yaml') + # check_file检查文件是否存在 + opt.data = check_file(opt.data) # check file + print(opt) + + # task = ['val', 'test']时就正常测试验证集、测试集 + if opt.task in ['val', 'test']: # run normally + test(opt.data, + opt.weights, + opt.batch_size, + opt.img_size, + opt.conf_thres, + opt.iou_thres, + opt.save_json, + opt.single_cls, + opt.augment, + opt.verbose) + + # task == 'study'时，就评估yolov5系列和yolov3-spp各个模型在各个尺度下的指标并可视化 + elif opt.task == 'study': # run over a range of settings and save/plot + for weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.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) + y.append(r + t) # results and times + np.savetxt(f, y, fmt='%10.4g') # save + os.system('zip -r study.zip study_*.txt') + # utils.general.plot_study_txt(f, x) # plot diff --git a/train.py b/train.py new file mode 100644 index 00000000..9a3c35a4 --- /dev/null +++ b/train.py @@ -0,0 +1,838 @@ +import argparse +import logging +import math +import os +import random +import shutil +import time +from pathlib import Path + +import numpy as np +import torch.distributed as dist +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.yolo import Model +from utils.datasets import create_dataloader +from utils.general import ( + torch_distributed_zero_first, labels_to_class_weights, plot_labels, check_anchors, labels_to_image_weights, + compute_loss, plot_images, fitness, strip_optimizer, plot_results, get_latest_run, check_dataset, check_file, + check_git_status, check_img_size, increment_dir, print_mutation, plot_evolution, set_logging, init_seeds) +from utils.google_utils import attempt_download +from utils.torch_utils import ModelEMA, select_device, intersect_dicts + +logger = logging.getLogger(__name__) + + +def train(hyp, opt, device, tb_writer=None): + logger.info(f'Hyperparameters {hyp}') + """ + 获取记录训练日志的路径: + 训练日志包括：权重、tensorboard文件、超参数hyp、设置的训练参数opt(也就是epochs,batch_size等),result.txt + result.txt包括: 占GPU内存、训练集的GIOU loss, objectness loss, classification loss, 总loss, + targets的数量, 输入图片分辨率, 准确率TP/(TP+FP),召回率TP/P ; + 测试集的mAP50, mAP@0.5:0.95, GIOU loss, objectness loss, classification loss. + 还会保存batch<3的ground truth + """ + # 如果设置进化算法则不会传入tb_writer(则为None)，设置一个evolve文件夹作为日志目录 + log_dir = Path(tb_writer.log_dir) if tb_writer else Path(opt.logdir) / 'evolve' # logging directory + + # 设置生成文件的保存路径 + wdir = log_dir / 'weights' # weights directory + os.makedirs(wdir, exist_ok=True) + last = wdir / 'last.pt' + best = wdir / 'best.pt' + results_file = str(log_dir / 'results.txt') + + # 获取轮次、批次、总批次(涉及到分布式训练)、权重、进程序号(主要用于分布式训练) + epochs, batch_size, total_batch_size, weights, rank = \ + opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank + + # Save run settings + # 保存hyp和opt + with open(log_dir / 'hyp.yaml', 'w') as f: + yaml.dump(hyp, f, sort_keys=False) + with open(log_dir / 'opt.yaml', 'w') as f: + yaml.dump(vars(opt), f, sort_keys=False) + + # Configure + # 获取数据路径 + cuda = device.type != 'cpu' + # 设置随机种子 + # 需要在每一个进程设置相同的随机种子，以便所有模型权重都初始化为相同的值，即确保神经网络每次初始化都相同 + init_seeds(2 + rank) + # 加载数据配置信息 + with open(opt.data) as f: + data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict + + # torch_distributed_zero_first同步所有进程 + # check_dataset检查数据集，如果没找到数据集则下载数据集(仅适用于项目中自带的yaml文件数据集) + with torch_distributed_zero_first(rank): + check_dataset(data_dict) # check + + # 获取训练集、测试集图片路径 + train_path = data_dict['train'] + test_path = data_dict['val'] + + # 获取类别数量和类别名字 + # 如果设置了opt.single_cls则为一类 + nc, names = (1, ['item']) if opt.single_cls else (int(data_dict['nc']), data_dict['names']) # 保存data.yaml中的number classes, names + assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check + + # Model + # 判断weights字符串是否以'.pt'为结尾。若是，则说明本次训练需要预训练模型 + pretrained = weights.endswith('.pt') + if pretrained: + # 加载模型，从google云盘中自动下载模型 + # 但通常会下载失败，建议提前下载下来放进weights目录 + with torch_distributed_zero_first(rank): + attempt_download(weights) # download if not found locally + ckpt = torch.load(weights, map_location=device) # load checkpoint 导入权重文件 + """ + 这里模型创建，可通过opt.cfg，也可通过ckpt['model'].yaml + 这里的区别在于是否是resume，resume时会将opt.cfg设为空， + 则按照ckpt['model'].yaml创建模型； + 这也影响着下面是否除去anchor的key(也就是不加载anchor)， + 如果resume，则加载权重中保存的anchor来继续训练； + 主要是预训练权重里面保存了默认coco数据集对应的anchor， + 如果用户自定义了anchor，再加载预训练权重进行训练，会覆盖掉用户自定义的anchor； + 所以这里主要是设定一个，如果加载预训练权重进行训练的话，就去除掉权重中的anchor，采用用户自定义的； + 如果是resume的话，就是不去除anchor，就权重和anchor一起加载， 接着训练； + 参考https://github.com/ultralytics/yolov5/issues/459 + 所以下面设置了intersect_dicts，该函数就是忽略掉exclude中的键对应的值 + """ + ''' + ckpt: + {'epoch': -1, + 'best_fitness': array([ 0.49124]), + 'training_results': None, + 'model': Model( + ... + ) + 'optimizer': None + } + ''' + if hyp.get('anchors'): # 用户自定义的anchors优先级大于权重文件中自带的anchors + ckpt['model'].yaml['anchors'] = round(hyp['anchors']) # force autoanchor + # 创建并初始化yolo模型 + model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device) # create + ''' + model = + Model( + (model): Sequential( + (0): Focus(...) + ... + (24): Detect(...) + ) + ) + ''' + # 如果opt.cfg存在，或重新设置了'anchors'，则将预训练权重文件中的'anchors'参数清除，使用用户自定义的‘anchors’信息 + exclude = ['anchor'] if opt.cfg or hyp.get('anchors') else [] # exclude keys + # state_dict变量存放训练过程中需要学习的权重和偏执系数，state_dict 是一个python的字典格式,以字典的格式存储,然后以字典的格式被加载,而且只加载key匹配的项 + # 将ckpt中的‘model’中的”可训练“的每一层的参数建立映射关系（如 'conv1.weight'： 数值...）存在state_dict中 + state_dict = ckpt['model'].float().state_dict() # to FP32 + # 加载除了与exclude以外，所有与key匹配的项的参数 即将权重文件中的参数导入对应层中 + state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect + # 将最终模型参数导入yolo模型 + 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: + # 不进行预训练，则直接创建并初始化yolo模型 + model = Model(opt.cfg, ch=3, nc=nc).to(device) # create + + # Freeze + #freeze = ['', ] # parameter names to freeze (full or partial) + freeze = ['model.%s.' % x for x in range(10)] # 冻结带有'model.0.'-'model.9.'的所有参数 即冻结0-9层的backbone + if any(freeze): + for k, v in model.named_parameters(): + if any(x in k for x in freeze): + print('freezing %s' % k) + v.requires_grad = False + + # Optimizer + """ + nbs人为模拟的batch_size; + 就比如默认的话上面设置的opt.batch_size为16,这个nbs就为64， + 也就是模型梯度累积了64/16=4(accumulate)次之后 + 再更新一次模型，变相的扩大了batch_size + """ + nbs = 64 # nominal batch size + accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing + # 根据accumulate设置权重衰减系数 + hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay + + pg0, pg1, pg2 = [], [], [] # optimizer parameter groups + # 将模型分成三组(w权重参数（非bn层）, bias, 其他所有参数)优化 + for k, v in model.named_parameters(): # named_parameters：网络层的名字和参数的迭代器 + ''' + (0): Focus( + (conv): Conv( + (conv): Conv2d(12, 80, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): BatchNorm2d(80, eps=0.001, momentum=0.03, affine=True, track_running_stats=True) + (act): Hardswish() + ) + ) + k: 网络层可训练参数的名字所属 如： model.0.conv.conv.weight 或 model.0.conv.bn.weight 或 model.0.conv.bn.bias （Focus层举例） + v： 对应网络层的具体参数 如：对应model.0.conv.conv.weight的 size为(80，12，3，3)的参数数据 即 卷积核的数量为80，深度为12，size为3×3 + ''' + v.requires_grad = True # 设置当前参数在训练时保留梯度信息 + if '.bias' in k: + pg2.append(v) # biases (所有的偏置参数) + elif '.weight' in k and '.bn' not in k: + pg1.append(v) # apply weight decay (非bn层的权重参数w) + else: + pg0.append(v) # all else （网络层的其他参数） + + # 选用优化器，并设置pg0组的优化方式 + if opt.adam: + 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) + + # 设置权重参数weights（非bn层）的优化方式 + optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay + # 设置偏置参数bias的优化方式 + optimizer.add_param_group({'params': pg2}) # add pg2 (biases) + logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0))) + del pg0, pg1, pg2 + + # 设置学习率衰减，这里为余弦退火方式进行衰减 + # 就是根据以下公式lf,epoch和超参数hyp['lrf']进行衰减 + # 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 匿名余弦退火函数 + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) + # plot_lr_scheduler(optimizer, scheduler, epochs) + + # Resume + # 初始化开始训练的epoch和最好的结果 + # best_fitness是以[0.0, 0.0, 0.1, 0.9]为系数并乘以[精确度, 召回率, mAP@0.5, mAP@0.5:0.95]再求和所得 + # 根据best_fitness来保存best.pt + start_epoch, best_fitness = 0, 0.0 + if pretrained: + # Optimizer + # 加载优化器与best_fitness + if ckpt['optimizer'] is not None: + optimizer.load_state_dict(ckpt['optimizer']) + best_fitness = ckpt['best_fitness'] + + # Results + # 加载训练结果result.txt + if ckpt.get('training_results') is not None: + with open(results_file, 'w') as file: + file.write(ckpt['training_results']) # write results.txt + + # Epochs + # 加载上次断点模型中训练的轮次，并在此基础上继续训练 + start_epoch = ckpt['epoch'] + 1 + + # 如果使用断点重训的同时发现 start_epoch= 0，则说明上次训练正常结束，不存在断点 + if opt.resume: + assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs) + shutil.copytree(wdir, wdir.parent / f'weights_backup_epoch{start_epoch - 1}') # save previous weights + + # 如果新设置epochs小于加载的epoch，则视新设置的epochs为需要再训练的轮次数而不再是总的轮次数 + if epochs < start_epoch: + 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, state_dict + + # Image sizes + # 获取模型总步长和模型输入图片分辨率 + gs = int(max(model.stride)) # grid size (max stride) + # 检查输入图片分辨率确保能够整除总步长gs + imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples + + # DP mode + # 分布式训练,参照:https://github.com/ultralytics/yolov5/issues/475 + # DataParallel模式,仅支持单机多卡,不支持混合精度训练 + # rank为进程编号, 这里应该设置为rank=-1则使用DataParallel模式 + # 如果 当前运行设备为gpu 且 进程编号=-1 且gpu数量大于1时 才会进行分布式训练 ，将model对象放入DataParallel容器即可进行分布式训练 + if cuda and rank == -1 and torch.cuda.device_count() > 1: + model = torch.nn.DataParallel(model) + + # SyncBatchNorm + # 实现多GPU之间的BatchNorm + if opt.sync_bn and cuda and rank != -1: + model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) + logger.info('Using SyncBatchNorm()') + + # Exponential moving average + ''' + EMA ： YOLOv5优化策略之一 + EMA + SGD可提高模型鲁棒性 + 为模型创建EMA指数滑动平均,如果GPU进程数大于1,则不创建 + ''' + ema = ModelEMA(model) if rank in [-1, 0] else None + + # DDP mode + # 如果rank不等于-1,则使用DistributedDataParallel模式 + # local_rank为gpu编号,rank为进程,例如rank=3，local_rank=0 表示第 3 个进程内的第 1 块 GPU。 + if cuda and rank != -1: + model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank) + + # Trainloader + # class dataloader 和 dataset . + 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) + + # 获取标签中最大的类别值，并于类别数作比较, 如果小于类别数则表示有问题 + 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) + + ''' + dataloader和testloader不同之处在于： + 1. testloader：没有数据增强，rect=True（大概是测试图片保留了原图的长宽比） + 2. dataloader：数据增强，保留了矩形框训练。 + ''' + # Process 0 + if rank in [-1, 0]: + # local_rank is set to -1. Because only the first process is expected to do evaluation. + # testloader + ema.updates = start_epoch * nb // accumulate # set EMA updates + # testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt, + # hyp=hyp, augment=False, cache=opt.cache_images and not opt.notest, rect=True, + # rank=-1, world_size=opt.world_size, workers=opt.workers)[0] # testloader + + 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)) + plot_labels(labels, save_dir=log_dir) + if tb_writer: + # tb_writer.add_hparams(hyp, {}) # causes duplicate https://github.com/ultralytics/yolov5/pull/384 + tb_writer.add_histogram('classes', c, 0) + + # Anchors + if not opt.noautoanchor: + check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) + + # Model parameters + # 根据自己数据集的类别数设置分类损失的系数 + hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset + # 设置类别数，超参数 + model.nc = nc # attach number of classes to model + model.hyp = hyp # attach hyperparameters to model + """ + 设置giou的值在objectness loss中做标签的系数, 使用代码如下 + tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * giou.detach().clamp(0).type(tobj.dtype) + 这里model.gr=1，也就是说完全使用标签框与预测框的giou值来作为该预测框的objectness标签 + """ + model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou) + # 根据labels初始化图片采样权重（图像类别所占比例高的采样频率低） + model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights + # 获取类别的名字 + model.names = names + + # Start training + t0 = time.time() + # 获取warm-up训练的迭代次数 + nw = max(round(hyp['warmup_epochs'] * nb), 1e3) # number of warmup iterations, max(3 epochs, 1k iterations) + # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training + # 初始化mAP和results + maps = np.zeros(nc) # mAP per class + results = (0, 0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls, angleloss) + """ + 设置学习率衰减所进行到的轮次， + 目的是打断训练后，--resume接着训练也能正常的衔接之前的训练进行学习率衰减 + """ + scheduler.last_epoch = start_epoch - 1 # do not move + # 通过torch1.6自带的api设置混合精度训练 + scaler = amp.GradScaler(enabled=cuda) + """ + 打印训练和测试输入图片分辨率 + 加载图片时调用的cpu进程数 + 从哪个epoch开始训练 + """ + logger.info('Image sizes %g train, %g test\nUsing %g dataloader workers\nLogging results to %s\n' + 'Starting training for %g epochs...' % (imgsz, imgsz_test, dataloader.num_workers, log_dir, epochs)) + + # 训练 + for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ + # model设置为训练模式，其中training属性表示BatchNorm与Dropout层在训练阶段和测试阶段中采取的策略不同，通过判断training值来决定前向传播策略 + model.train() + + # Update image weights (optional) + # 加载图片权重（可选） + if opt.image_weights: + # Generate indices + """ + 如果设置进行图片采样策略， + 则根据前面初始化的图片采样权重model.class_weights以及maps配合每张图片包含的类别数 + 通过random.choices生成图片索引indices从而进行采样 + """ + 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 + # 如果是DDP模式,则广播采样策略 + 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() + + # 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 + + # 初始化训练时打印的平均损失信息 + mloss = torch.zeros(5, device=device) # mean losses + if rank != -1: + # DDP模式下打乱数据, ddp.sampler的随机采样数据是基于epoch+seed作为随机种子， + # 每次epoch不同，随机种子就不同 + dataloader.sampler.set_epoch(epoch) + pbar = enumerate(dataloader) + logger.info(('\n' + '%10s' * 9) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'angle', 'total', 'targets', 'img_size')) + if rank in [-1, 0]: + # tqdm 创建进度条，方便训练时 信息的展示 + pbar = tqdm(pbar, total=nb) # progress bar + optimizer.zero_grad() + for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------ + ''' + i: batch_index, 第i个batch + imgs : torch.Size([batch_size, 3, resized_height, resized_weight]) + targets : torch.Size = (该batch中的目标数量, [该image属于该batch的第几个图片, class, xywh, θ]) + paths : List['img1_path','img2_path',......,'img-1_path'] len(paths)=batch_size + shapes : size= batch_size, 不进行mosaic时进行矩形训练时才有值 + ''' + # ni计算迭代的次数iteration + ni = i + nb * epoch # number integrated batches (since train start) + imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0 + + # Warmup + """ + warmup训练(前nw次迭代) + 在前nw次迭代中，根据以下方式选取accumulate和学习率 + """ + 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的学习率从0.1下降到基准学习率lr*lf(epoch)， + 其他的参数学习率从0增加到lr*lf(epoch). + lf为上面设置的余弦退火的衰减函数 + """ + # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 + x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)]) + if 'momentum' in x: + x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) + + # Multi-scale + # 设置多尺度训练，从imgsz * 0.5, imgsz * 1.5 + gs随机选取尺寸 + if opt.multi_scale: + 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 gs-multiple) + # 采用上采样下采样函数interpolate完成imgs尺寸的转变，模式设置为双线性插值 + imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) + + # Forward + # 前向传播 + with amp.autocast(enabled=cuda): + ''' + 训练时返回x + x list: [small_forward, medium_forward, large_forward] eg:small_forward.size=( batch_size, 3种scale框, size1, size2, no) + ''' + pred = model(imgs) # forward + # Loss + # 计算损失，包括分类损失，objectness损失，框的回归损失 + # loss为总损失值，loss_items为一个元组(lbox, lobj, lcls, langle, loss) + loss, loss_items = compute_loss(pred, targets.to(device), model, csl_label_flag=True) # loss scaled by batch_size + if rank != -1: + # 平均不同gpu之间的梯度 + loss *= opt.world_size # gradient averaged between devices in DDP mode + + # Backward + scaler.scale(loss).backward() + + # Optimize + # 模型反向传播accumulate次之后再根据累积的梯度更新一次参数 + if ni % accumulate == 0: + scaler.step(optimizer) # optimizer.step + scaler.update() + optimizer.zero_grad() + if ema: + ema.update(model) + + # Print + if rank in [-1, 0]: + # mloss (lbox, lobj, lcls, langle, loss) + # 打印显存，进行的轮次，损失，target的数量和图片的size等信息 + 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' * 7) % ( + '%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1]) + # 进度条显示以上信息 + pbar.set_description(s) + + # Plot + # 将前三次迭代batch的标签框在图片上画出来并保存 + if ni < 3: + f = str(log_dir / ('train_batch%g.jpg' % ni)) # filename + result = plot_images(images=imgs, targets=targets, paths=paths, fname=f) + if tb_writer and result is not None: + tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch) # 存储的格式为[H, W, C] + # tb_writer.add_graph(model, imgs) # add model to tensorboard + + # end batch ------------------------------------------------------------------------------------------------ + + # Scheduler + lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard + scheduler.step() + + # DDP process 0 or single-GPU + if rank in [-1, 0]: + # mAP + if ema: + # 更新EMA的属性 + # 添加include的属性 + ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride']) + final_epoch = epoch + 1 == epochs + # # 判断该epoch是否为最后一轮 + # if not opt.notest or final_epoch: # Calculate mAP + # # 对测试集进行测试，计算mAP等指标 + # # 测试时使用的是EMA模型 + # 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=log_dir, + # plots=epoch == 0 or final_epoch) # plot first and last + + # Write + # 将测试指标写入result.txt + with open(results_file, 'a') as f: + f.write(s + '%10.4g' * 8 % 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 + # 添加指标，损失等信息到tensorboard显示 + if tb_writer: + tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', 'train/angle_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/angle_loss', # val loss + 'x/lr0', 'x/lr1', 'x/lr2'] # params + for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags): + tb_writer.add_scalar(tag, x, epoch) + + # Update best mAP + # 更新best_fitness + 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 + """ + 保存模型，还保存了epoch，results，optimizer等信息， + optimizer信息在最后一轮完成后不会进行保存 未完成训练则保存该信息 + model保存的是EMA的模型 + """ + 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()} + + # Save last, best and delete + torch.save(ckpt, last) + if best_fitness == fi: + torch.save(ckpt, best) + del ckpt + # end epoch ---------------------------------------------------------------------------------------------------- + # end training + + if rank in [-1, 0]: + # Strip optimizers + """ + 模型训练完后，strip_optimizer函数将optimizer从ckpt中去除； + 并且对模型进行model.half(), 将Float32的模型->Float16， + 可以减少模型大小，提高inference速度 + """ + n = opt.name if opt.name.isnumeric() else '' + fresults, flast, fbest = log_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 os.path.exists(f1): + os.rename(f1, f2) # rename + if str(f2).endswith('.pt'): # is *.pt + strip_optimizer(f2) # strip optimizer + # 上传结果到谷歌云盘 + os.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket else None # upload + + # Finish + # 可视化results.txt文件 + if not opt.evolve: + plot_results(save_dir=log_dir) # save as results.png + logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600)) + + # 释放显存 + dist.destroy_process_group() if rank not in [-1, 0] else None + torch.cuda.empty_cache() + return results + + +if __name__ == '__main__': + """ + opt参数解析： + weights:加载的权重文件 + cfg:模型配置文件，网络结构 + data:数据集配置文件，数据集路径，类名等 + hyp:超参数文件 + epochs:训练总轮次 + batch-size:批次大小 + img-size:输入图片分辨率大小 + rect:是否采用矩形训练，默认False + resume:接着打断训练上次的结果接着训练 + nosave:不保存模型，默认False + notest:不进行test，默认False + noautoanchor:不自动调整anchor，默认False + evolve:是否进行超参数进化，默认False + bucket:谷歌云盘bucket，一般不会用到 + cache-images:是否提前缓存图片到内存，以加快训练速度，默认False + name:数据集名字，如果设置：results.txt to results_name.txt，默认无 + device:训练的设备，cpu；0(表示一个gpu设备cuda:0)；0,1,2,3(多个gpu设备) + multi-scale:是否进行多尺度训练，默认False + single-cls:数据集是否只有一个类别，默认False + adam:是否使用adam优化器 + sync-bn:是否使用跨卡同步BN,在DDP模式使用 + local_rank:gpu编号 + logdir:存放日志的目录 + workers:dataloader的最大worker数量 + """ + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default='./weights/yolov5m.pt',help='initil weights path') + parser.add_argument('--cfg', type=str, default='', help='model.yaml path') + parser.add_argument('--data', type=str, default='data/DOTA_ROTATED.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=150) + parser.add_argument('--batch-size', type=int, default=4, help='total batch size for all GPUs') + parser.add_argument('--img-size', nargs='+', type=int, default=[1024, 1024], help='[train, test] image sizes') + parser.add_argument('--rect', action='store_true', help='rectangular training') + parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') + parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') + parser.add_argument('--notest', action='store_true', default=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', default=False, help='cache images for faster training') + parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') + parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied') + parser.add_argument('--device', default='0', 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('--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('--logdir', type=str, default='runs/', help='logging directory') + parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers') + opt = parser.parse_args() + + # Set DDP variables + """ + 设置DDP(Distributed Data Parallel，分布式数据并行)模式的参数 + world_size:表示全局进程个数，可认为是gpu的数量 + global_rank:进程编号 + """ + 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() + + # Resume + ''' + 是否从断点开始训练 + ''' + if opt.resume: # resume an interrupted run + # 如果resume的参数是str,则表示传入的是最新的断点模型的路径地址,直接导入last模型进行训练 + # get_latest_run()函数获取runs文件夹中最近的last.pt，从断点处导入继续训练 + ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path + log_dir = Path(ckpt).parent.parent # runs/exp0 + assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist' + # opt参数也全部替换 + with open(log_dir / 'opt.yaml') as f: + opt = argparse.Namespace(**yaml.load(f, Loader=yaml.FullLoader)) # replace + + # opt.cfg设置为'' 对应着train函数里面的操作(加载权重时是否加载权重里的anchor) + opt.cfg, opt.weights, opt.resume = '', ckpt, True + logger.info('Resuming training from %s' % ckpt) + + else: + # # 获取超参数列表 + # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml') + # 检查数据集参数信息文件、cfg配置文件、训练超参数信息文件是否存在以及是否存在多个文件 + 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' + # 扩展image_size为[image_size, image_size]一个是训练size，一个是测试size + opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test) + log_dir = increment_dir(Path(opt.logdir) / 'exp', opt.name) # runs/exp1 训练日志存放路径 + # 选择设备 + device = select_device(opt.device, batch_size=opt.batch_size) + + # DDP mode + if opt.local_rank != -1: # -1 表示cpu + assert torch.cuda.device_count() > opt.local_rank + # 根据gpu编号选择设备 + torch.cuda.set_device(opt.local_rank) + device = torch.device('cuda', opt.local_rank) + # 初始化进程组 + dist.init_process_group(backend='nccl', init_method='env://') # distributed backend 一般来说使用NCCL对于GPU分布式训练，使用gloo对CPU进行分布式训练 + assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count' + # 将总批次按照进程数分配给各个gpu + opt.batch_size = opt.total_batch_size // opt.world_size + + # 打印opt参数信息 + logger.info(opt) + # 加载超参数列表 + with open(opt.hyp) as f: + hyp = yaml.load(f, Loader=yaml.FullLoader) # load hyps + + # Train + # 如果不进行超参数进化，则直接调用train()函数，开始训练 + if not opt.evolve: + tb_writer = None + if opt.global_rank in [-1, 0]: + # 创建tensorboard + logger.info('Start Tensorboard with "tensorboard --logdir %s", view at http://localhost:6006/' % opt.logdir) + tb_writer = SummaryWriter(log_dir=log_dir) # runs/exp0 + + train(hyp, opt, device, tb_writer) + + # Evolve hyperparameters (optional) + else: + # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) + # 超参数进化列表,括号里分别为(突变规模, 最小值,最大值) + 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 + 'angle': (1, 0.2, 4.0), + 'angle_pw': (1, 0.5, 2.0), + '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('runs/evolve/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 + + # 默认进化300次 + """ + 这里的进化算法是：根据之前训练时的hyp来确定一个base hyp再进行突变； + 如何根据？通过之前每次进化得到的results来确定之前每个hyp的权重 + 有了每个hyp和每个hyp的权重之后有两种进化方式； + 1.根据每个hyp的权重随机选择一个之前的hyp作为base hyp，random.choices(range(n), weights=w) + 2.根据每个hyp的权重对之前所有的hyp进行融合获得一个base hyp，(x * w.reshape(n, 1)).sum(0) / w.sum() + evolve.txt会记录每次进化之后的results+hyp + 每次进化时，hyp会根据之前的results进行从大到小的排序； + 再根据fitness函数计算之前每次进化得到的hyp的权重 + 再确定哪一种进化方式，从而进行进化 + """ + for _ in range(300): # generations to evolve + if os.path.exists('evolve.txt'): # if evolve.txt exists: select best hyps and mutate + # Select parent(s) + # 选择进化方式 + parent = 'single' # parent selection method: 'single' or 'weighted' + # 加载evolve.txt + x = np.loadtxt('evolve.txt', ndmin=2) + # 选取至多前5次进化的结果 + n = min(5, len(x)) # number of previous results to consider + x = x[np.argsort(-fitness(x))][:n] # top n mutations + # 根据results计算hyp的权重 + w = fitness(x) - fitness(x).min() # weights + # 根据不同进化方式获得base hyp + if parent == 'single' or len(x) == 1: + # x = x[random.randint(0, n - 1)] # random selection + x = x[random.choices(range(n), weights=w)[0]] # weighted selection + elif parent == 'weighted': + x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination + + # Mutate + # 超参数进化 + mp, s = 0.8, 0.2 # mutation probability, sigma + npr = np.random + npr.seed(int(time.time())) + # 获取突变初始值 + 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) + # 将突变添加到base hyp上 + # [i+7]是因为x中前七个数字为results的指标(P, R, mAP, F1, test_losses=(GIoU, obj, cls))，之后才是超参数hyp + for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) + hyp[k] = float(x[i + 7] * v[i]) # mutate + + # Constrain to limits + # 修剪hyp在规定范围里 + 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 + # 训练 + results = train(hyp.copy(), opt, device) + + # Write mutation results + """ + 写入results和对应的hyp到evolve.txt + evolve.txt文件每一行为一次进化的结果 + 一行中前七个数字为(P, R, mAP, F1, test_losses=(GIoU, obj, cls))，之后为hyp + 保存hyp到yaml文件 + """ + print_mutation(hyp.copy(), results, yaml_file, opt.bucket) + + # Plot results + plot_evolution(yaml_file) + print('Hyperparameter evolution complete. Best results saved as: %s\nCommand to train a new model with these ' + 'hyperparameters: $ python train.py --hyp %s' % (yaml_file, yaml_file)) diff --git a/tutorial.ipynb b/tutorial.ipynb new file mode 100644 index 00000000..d398de41 --- /dev/null +++ b/tutorial.ipynb @@ -0,0 +1,758 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "YOLOv5 Tutorial", + "provenance": [], + "collapsed_sections": [], + "toc_visible": true, + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "HvhYZrIZCEyo" + }, + "source": [ + "\n", + "\n", + "This notebook was written by Ultralytics LLC, and is freely available for redistribution under the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/). \n", + "For more information please visit https://github.com/ultralytics/yolov5 and https://www.ultralytics.com." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7mGmQbAO5pQb", + "colab_type": "text" + }, + "source": [ + "# Setup\n", + "\n", + "Clone repo, install dependencies, `%cd` into `./yolov5` folder and check GPU." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wbvMlHd_QwMG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "outputId": "669566b2-391f-4596-f290-110e2e177946" + }, + "source": [ + "!git clone https://github.com/ultralytics/yolov5 # clone repo\n", + "!pip install -qr yolov5/requirements.txt # install dependencies (ignore errors)\n", + "%cd yolov5\n", + "\n", + "import torch\n", + "from IPython.display import Image, clear_output # to display images\n", + "from utils.google_utils import gdrive_download # to download models/datasets\n", + "\n", + "clear_output()\n", + "print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Setup complete. Using torch 1.5.1+cu101 _CudaDeviceProperties(name='Tesla T4', major=7, minor=5, total_memory=15079MB, multi_processor_count=40)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "N3qM6T0W53gh", + "colab_type": "text" + }, + "source": [ + "# 1. Inference\n", + "\n", + "Run inference with a pretrained checkpoint on contents of `/inference/images` folder. Models are auto-downloaded from [Google Drive](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zR9ZbuQCH7FX", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 488 + }, + "outputId": "528fcc04-2393-437a-84d2-092becbaefbe" + }, + "source": [ + "!python detect.py --weights yolov5s.pt --img 416 --conf 0.4 --source inference/images/\n", + "Image(filename='inference/output/zidane.jpg', width=600)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.4, device='', fourcc='mp4v', half=False, img_size=416, iou_thres=0.5, output='inference/output', save_txt=False, source='./inference/images/', view_img=False, weights='yolov5s.pt')\n", + "Using CUDA device0 _CudaDeviceProperties(name='Tesla P100-PCIE-16GB', total_memory=16280MB)\n", + "\n", + "image 1/2 inference/images/bus.jpg: 416x352 3 persons, 1 buss, Done. (0.009s)\n", + "image 2/2 inference/images/zidane.jpg: 288x416 2 persons, 2 ties, Done. (0.009s)\n", + "Results saved to /content/yolov5/inference/output\n", + "Done. (0.100s)\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "image/jpeg": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [], + "image/jpeg": { + "width": 600 + } + }, + "execution_count": 14 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4JnkELT0cIJg", + "colab_type": "text" + }, + "source": [ + "Inference can be run on a variety of sources: images, videos, directories, webcams, rtsp and http streams as shown in the example below." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "WwosXmgDahte", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Example syntax (do not run cell)\n", + "!python detect.py --source file.jpg # image \n", + " file.mp4 # video\n", + " dir/ # directory\n", + " 0 # webcam\n", + " 'rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa' # rtsp\n", + " 'http://112.50.243.8/PLTV/88888888/224/3221225900/1.m3u8' # http" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0eq1SMWl6Sfn", + "colab_type": "text" + }, + "source": [ + "# 2. Test\n", + "Test a model on COCO val or test-dev dataset to determine trained accuracy. Models are auto-downloaded from [Google Drive](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J). To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be 1-2% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eyTZYGgRjnMc", + "colab_type": "text" + }, + "source": [ + "### 2.1 val2017\n", + "Download COCO val 2017 dataset, 1GB, 5000 images, and test model accuracy." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "WQPtK1QYVaD_", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 33 + }, + "outputId": "df037a5d-efae-4687-9ff7-22a48fd7f801" + }, + "source": [ + "# Download COCO val2017\n", + "gdrive_download('1Y6Kou6kEB0ZEMCCpJSKStCor4KAReE43','coco2017val.zip') # val2017 dataset\n", + "!mv ./coco ../ # move folder alongside /yolov5" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Downloading https://drive.google.com/uc?export=download&id=1Y6Kou6kEB0ZEMCCpJSKStCor4KAReE43 as coco2017val.zip... unzipping... Done (11.2s)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "X58w8JLpMnjH", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 606 + }, + "outputId": "8c62a086-e312-46d1-b475-d90542eae545" + }, + "source": [ + "# Run YOLOv5x on COCO val2017\n", + "!python test.py --weights yolov5x.pt --data coco.yaml --img 672" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', img_size=672, iou_thres=0.65, merge=False, save_json=True, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n", + "Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)\n", + "\n", + "Fusing layers... Model Summary: 284 layers, 8.89222e+07 parameters, 8.89222e+07 gradients\n", + "Scanning labels ../coco/labels/val2017.cache (4952 found, 0 missing, 48 empty, 0 duplicate, for 5000 images): 100% 5000/5000 [00:00<00:00, 22899.17it/s]\n", + " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 157/157 [02:38<00:00, 1.01s/it]\n", + " all 5e+03 3.63e+04 0.426 0.746 0.66 0.469\n", + "Speed: 22.3/1.7/24.0 ms inference/NMS/total per 672x672 image at batch-size 32\n", + "\n", + "COCO mAP with pycocotools... saving detections_val2017__results.json...\n", + "loading annotations into memory...\n", + "Done (t=0.41s)\n", + "creating index...\n", + "index created!\n", + "Loading and preparing results...\n", + "DONE (t=4.39s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=76.56s).\n", + "Accumulating evaluation results...\n", + "DONE (t=11.02s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.484\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.668\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.528\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.311\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.534\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.628\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.371\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.609\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.662\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.501\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.714\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.807\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rc_KbFk0juX2", + "colab_type": "text" + }, + "source": [ + "### 2.2 test-dev2017\n", + "Download COCO test2017 dataset, 7GB, 40,000 images, to test model accuracy on test-dev set, 20,000 images. Results are saved to a `*.json` file which can be submitted to the evaluation server at https://competitions.codalab.org/competitions/20794." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "V0AJnSeCIHyJ", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Download COCO test-dev2017\n", + "gdrive_download('1cXZR_ckHki6nddOmcysCuuJFM--T-Q6L','coco2017labels.zip') # annotations\n", + "!f=\"test2017.zip\" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f # 7GB, 41k images\n", + "!mv ./test2017 ./coco/images && mv ./coco ../ # move images into /coco and move /coco alongside /yolov5" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "29GJXAP_lPrt", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Run YOLOv5s on COCO test-dev2017 with argument --task test\n", + "!python test.py --weights yolov5s.pt --data ./data/coco.yaml --task test" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VUOiNLtMP5aG", + "colab_type": "text" + }, + "source": [ + "# 3. Train\n", + "\n", + "Download https://www.kaggle.com/ultralytics/coco128, a small 128-image tutorial dataset, start tensorboard and train YOLOv5s from a pretrained checkpoint for 3 epochs (actual training is much longer, around **300-1000 epochs**, depending on your dataset)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Knxi2ncxWffW", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 33 + }, + "outputId": "35815e93-7d6e-4fee-c050-a4a565d51648" + }, + "source": [ + "# Download coco128\n", + "gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f','coco128.zip') # coco128 dataset\n", + "!mv ./coco128 ../ # move folder alongside /yolov5" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Downloading https://drive.google.com/uc?export=download&id=1n_oKgR81BJtqk75b00eAjdv03qVCQn2f as coco128.zip... unzipping... Done (5.3s)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_pOkGLv1dMqh", + "colab_type": "text" + }, + "source": [ + "Train a YOLOv5s model on coco128 by specifying model config file `--cfg models/yolo5s.yaml`, and dataset config file `--data data/coco128.yaml`. Start training from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights ''`. Pretrained weights are auto-downloaded from [Google Drive](https://drive.google.com/open?id=1Drs_Aiu7xx6S-ix95f9kNsA6ueKRpN2J).\n", + "\n", + "**All training results are saved to `runs/exp0`** for the first experiment, then `runs/exp1`, `runs/exp2` etc. for subsequent experiments.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bOy5KI2ncnWd", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Start tensorboard (optional)\n", + "%load_ext tensorboard\n", + "%tensorboard --logdir runs" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "1NcFxRcFdJ_O", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "121b5b2e-bc8e-4648-ee1c-8d2795176db6" + }, + "source": [ + "# Train YOLOv5s on coco128 for 3 epochs\n", + "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --cfg yolov5s.yaml --weights yolov5s.pt --nosave --cache" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Namespace(batch_size=16, bucket='', cache_images=True, cfg='./models/yolov5s.yaml', data='./data/coco128.yaml', device='', epochs=3, evolve=False, hyp='', img_size=[640], multi_scale=False, name='', noautoanchor=False, nosave=True, notest=False, rect=False, resume=False, single_cls=False, weights='yolov5s.pt')\n", + "Using CUDA device0 _CudaDeviceProperties(name='Tesla T4', total_memory=15079MB)\n", + "\n", + "2020-07-11 20:37:09.422496: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n", + "Start Tensorboard with \"tensorboard --logdir=runs\", view at http://localhost:6006/\n", + "Hyperparameters {'optimizer': 'SGD', 'lr0': 0.01, 'momentum': 0.937, 'weight_decay': 0.0005, 'giou': 0.05, 'cls': 0.58, 'cls_pw': 1.0, 'obj': 1.0, 'obj_pw': 1.0, 'iou_t': 0.2, 'anchor_t': 4.0, 'fl_gamma': 0.0, 'hsv_h': 0.014, 'hsv_s': 0.68, 'hsv_v': 0.36, 'degrees': 0.0, 'translate': 0.0, 'scale': 0.5, 'shear': 0.0}\n", + "\n", + " from n params module arguments \n", + " 0 -1 1 3520 models.common.Focus [3, 32, 3] \n", + " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", + " 2 -1 1 19904 models.common.BottleneckCSP [64, 64, 1] \n", + " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", + " 4 -1 1 161152 models.common.BottleneckCSP [128, 128, 3] \n", + " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n", + " 6 -1 1 641792 models.common.BottleneckCSP [256, 256, 3] \n", + " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n", + " 8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]] \n", + " 9 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False] \n", + " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 12 [-1, 6] 1 0 models.common.Concat [1] \n", + " 13 -1 1 378624 models.common.BottleneckCSP [512, 256, 1, False] \n", + " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", + " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 16 [-1, 4] 1 0 models.common.Concat [1] \n", + " 17 -1 1 95104 models.common.BottleneckCSP [256, 128, 1, False] \n", + " 18 -1 1 32895 torch.nn.modules.conv.Conv2d [128, 255, 1, 1] \n", + " 19 -2 1 147712 models.common.Conv [128, 128, 3, 2] \n", + " 20 [-1, 14] 1 0 models.common.Concat [1] \n", + " 21 -1 1 313088 models.common.BottleneckCSP [256, 256, 1, False] \n", + " 22 -1 1 65535 torch.nn.modules.conv.Conv2d [256, 255, 1, 1] \n", + " 23 -2 1 590336 models.common.Conv [256, 256, 3, 2] \n", + " 24 [-1, 10] 1 0 models.common.Concat [1] \n", + " 25 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False] \n", + " 26 -1 1 130815 torch.nn.modules.conv.Conv2d [512, 255, 1, 1] \n", + " 27 [-1, 22, 18] 1 0 models.yolo.Detect [80, [[116, 90, 156, 198, 373, 326], [30, 61, 62, 45, 59, 119], [10, 13, 16, 30, 33, 23]]]\n", + "Model Summary: 191 layers, 7.46816e+06 parameters, 7.46816e+06 gradients\n", + "\n", + "Optimizer groups: 62 .bias, 70 conv.weight, 59 other\n", + "Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 100% 128/128 [00:00<00:00, 20484.22it/s]\n", + "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 156.07it/s]\n", + "Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 100% 128/128 [00:00<00:00, 22082.55it/s]\n", + "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 152.91it/s]\n", + "\n", + "Analyzing anchors... Best Possible Recall (BPR) = 0.9935\n", + "Image sizes 640 train, 640 test\n", + "Using 2 dataloader workers\n", + "Starting training for 3 epochs...\n", + "\n", + " Epoch gpu_mem GIoU obj cls total targets img_size\n", + " 0/2 6.84G 0.04376 0.06831 0.02 0.1321 225 640: 100% 8/8 [00:09<00:00, 1.22s/it]\n", + " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:09<00:00, 1.24s/it]\n", + " all 128 929 0.34 0.762 0.69 0.446\n", + "\n", + " Epoch gpu_mem GIoU obj cls total targets img_size\n", + " 1/2 6.06G 0.04333 0.08225 0.02207 0.1476 182 640: 100% 8/8 [00:03<00:00, 2.17it/s]\n", + " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:02<00:00, 3.28it/s]\n", + " all 128 929 0.342 0.755 0.687 0.447\n", + "\n", + " Epoch gpu_mem GIoU obj cls total targets img_size\n", + " 2/2 6.06G 0.0444 0.07251 0.01855 0.1355 216 640: 100% 8/8 [00:03<00:00, 2.15it/s]\n", + " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:02<00:00, 3.46it/s]\n", + " all 128 929 0.354 0.759 0.689 0.45\n", + "Optimizer stripped from runs/exp0/weights/last.pt, 15.2MB\n", + "3 epochs completed in 0.009 hours.\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DLI1JmHU7B0l", + "colab_type": "text" + }, + "source": [ + "# 4. Visualize\n", + "\n", + "View `runs/exp0/train*.jpg` images to see training images, labels and augmentation effects. A **Mosaic Dataloader** is used for training (shown below), a new concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "W40tI99_7BcH", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 917 + }, + "outputId": "1c838e44-79fe-433f-a334-59a037ee322e" + }, + "source": [ + "Image(filename='runs/exp0/train_batch1.jpg', width=900) # view augmented training mosaics" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "image/jpeg": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [], + "image/jpeg": { + "width": 900 + } + }, + "execution_count": 17 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7uOowJKI-Qak", + "colab_type": "text" + }, + "source": [ + "View `test_batch0_gt.jpg` to see test batch 0 *ground truth* labels." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "PF9MLHDb7tB6", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 647 + }, + "outputId": "b7a874f7-dad3-4611-e777-56c724c7ee81" + }, + "source": [ + "Image(filename='runs/exp0/test_batch0_gt.jpg', width=900) # view test image labels" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "image/jpeg": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [], + "image/jpeg": { + "width": 900 + } + }, + "execution_count": 20 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EGrb16Mu-jif", + "colab_type": "text" + }, + "source": [ + "View `test_batch0_pred.jpg` to see test batch 0 *predictions*." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ycP4UTEZ82_I", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 647 + }, + "outputId": "c7c1238d-e0fa-4fc5-f393-bf5bce55d245" + }, + "source": [ + "Image(filename='runs/exp0/test_batch0_pred.jpg', width=900) # view test image predictions" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "image/jpeg": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [], + "image/jpeg": { + "width": 900 + } + }, + "execution_count": 19 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7KN5ghjE6ZWh", + "colab_type": "text" + }, + "source": [ + "Training losses and performance metrics are saved to Tensorboard and also to a `runs/exp0/results.txt` logfile. `results.txt` is plotted as `results.png` after training completes. Partially completed `results.txt` files can be plotted with `from utils.general import plot_results; plot_results()`. Here we show YOLOv5s trained on coco128 to 300 epochs, starting from scratch (blue), and from pretrained `yolov5s.pt` (orange)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MDznIqPF7nk3", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 517 + }, + "outputId": "c1146425-643e-49ab-de25-73216f0dde23" + }, + "source": [ + "from utils.general import plot_results; plot_results() # plot results.txt files as results.png" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [], + "image/png": { + "width": 1000 + } + }, + "execution_count": 29 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zelyeqbyt3GD", + "colab_type": "text" + }, + "source": [ + "# Environments\n", + "\n", + "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):\n", + "\n", + "- **Google Colab Notebook** with free GPU: \"Open\n", + "- **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov5](https://www.kaggle.com/ultralytics/yolov5)\n", + "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) \n", + "- **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IEijrePND_2I", + "colab_type": "text" + }, + "source": [ + "# Appendix\n", + "\n", + "Optional extras below. Unit tests validate repo functionality and should be run on any PRs submitted.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gI6NoBev8Ib1", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Re-clone repo\n", + "%cd ..\n", + "!rm -rf yolov5 && git clone https://github.com/ultralytics/yolov5\n", + "%cd yolov5" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Z2AvpeKfrbsT", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# Test GCP ckpt\n", + "%%shell\n", + "for x in best*\n", + "do\n", + " gsutil cp gs://*/*/*/$x.pt .\n", + " python test.py --weights $x.pt --data coco.yaml --img 672\n", + "done" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "FGH0ZjkGjejy", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# YOLOv5 unit tests\n", + "%%shell\n", + "cd .. && rm -rf yolov5 && git clone https://github.com/ultralytics/yolov5 && cd yolov5\n", + "export PYTHONPATH=\"$PWD\" # to run *.py. files in subdirectories\n", + "pip install -qr requirements.txt onnx\n", + "python3 -c \"from utils.google_utils import *; gdrive_download('1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', 'coco128.zip')\" && mv ./coco128 ../\n", + "for x in yolov5s #yolov5m yolov5l yolov5x # models\n", + "do\n", + " python train.py --weights $x.pt --cfg $x.yaml --epochs 4 --img 320 --device 0 # train\n", + " for di in 0 cpu # inference devices\n", + " do\n", + " python detect.py --weights $x.pt --device $di # detect official\n", + " python detect.py --weights runs/exp0/weights/last.pt --device $di # detect custom\n", + " python test.py --weights $x.pt --device $di # test official\n", + " python test.py --weights runs/exp0/weights/last.pt --device $di # test custom\n", + " done\n", + " python models/yolo.py --cfg $x.yaml # inspect\n", + " python models/export.py --weights $x.pt --img 640 --batch 1 # export\n", + "done" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/utils/activations.py b/utils/activations.py new file mode 100644 index 00000000..162cb9fc --- /dev/null +++ b/utils/activations.py @@ -0,0 +1,70 @@ +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))) diff --git a/utils/datasets.py b/utils/datasets.py new file mode 100644 index 00000000..3798bf44 --- /dev/null +++ b/utils/datasets.py @@ -0,0 +1,1199 @@ +import glob +import math +import os +import random +import shutil +import time +from pathlib import Path +from threading import Thread + +import cv2 +import numpy as np +import torch +from PIL import Image, ExifTags +from torch.utils.data import Dataset +from tqdm import tqdm + +from utils.general import xyxy2xywh, xywh2xyxy, torch_distributed_zero_first, cvminAreaRect2longsideformat, longsideformat2cvminAreaRect + +help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' +img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.dng'] +vid_formats = ['.mov', '.avi', '.mp4', '.mpg', '.mpeg', '.m4v', '.wmv', '.mkv'] + +# Get orientation exif tag +for orientation in ExifTags.TAGS.keys(): + if ExifTags.TAGS[orientation] == 'Orientation': + 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) + try: + rotation = dict(img._getexif().items())[orientation] + if rotation == 6: # rotation 270 + s = (s[1], s[0]) + elif rotation == 8: # rotation 90 + s = (s[1], s[0]) + except: + pass + + return s + + +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): + ''' + 确保只有DDP中的第一个进程首先处理数据集，然后其他进程可以使用缓存。 + Make sure only the first process in DDP process the dataset first, and the following others can use the cache. + return: + dataloader ： 数据加载器，结合了数据集和取样器 + i: batch_index, 第i个batch (索引方式) 以下为具体数据加载器中的内容 + imgs : torch.Size([batch_size, 3, resized_noheight, resized_width]) + targets : torch.Size = (该batch中的目标数量, [该image属于该batch的第几个图片, class, 经归一化后的xywh]) + paths : List['img1_path','img2_path',......,'img-1_path'] len(paths)=batch_size + shapes ： size= batch_size, 不进行mosaic时进行矩形训练时才有值 + Class dataset 其中有: + self.img_files 路径文件夹下所有图片路径 self.img_files=['??\\images\\train2017\\1.jpg',...,] + self.label_files 路径文件夹下所有label_txt路径 self.label_files=['??\\labels\\train2017\\1.txt',...,] + self.n 路径文件夹下所有图片的总数量 + self.batch , self.img_size , self.augment , self.hyp , self.image_weights , self.rect , self.mosaic , self.mosaic_border , self.stride , + self.shapes [[1.jpg的形状]...[n.jpg的形状]] eg：[[480 80][360 640]...[480 640]] + self.labels [array( [对应1.txt的labels信息] ，dtype=float32), ..., array( [对应n.txt的labels信息] ，dtype=float32)] + ''' + 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) + + 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 + dataloader = InfiniteDataLoader(dataset, + # 从数据库中每次抽出batch size个样本 + batch_size=batch_size, + num_workers=nw, + sampler=sampler, + pin_memory=True, + collate_fn=LoadImagesAndLabels.collate_fn) # torch.utils.data.DataLoader() + 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 + ''' + for inference. LoadImages(path, img_size=640) + + ''' + 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 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) + + self.img_size = img_size + self.files = images + videos + 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' % \ + (p, img_formats, vid_formats) + + def __iter__(self): + self.count = 0 + return self + + def __next__(self): + ''' + return path, img, img0, self.cap + 返回路径，resize+pad的图片，原始图片，视频对象 + ''' + if self.count == self.nf: + raise StopIteration + path = self.files[self.count] + + if self.video_flag[self.count]: + # Read video + self.mode = 'video' + ret_val, img0 = self.cap.read() + if not ret_val: + self.count += 1 + self.cap.release() + if self.count == self.nf: # last video + raise StopIteration + else: + path = self.files[self.count] + self.new_video(path) + 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='') + + 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='') + + # Padded resize + img = letterbox(img0, new_shape=self.img_size)[0] + + # Convert + 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 + # 返回路径，resize+pad的图片，原始图片，视频对象 + return path, img, img0, self.cap + + def new_video(self, path): + self.frame = 0 + self.cap = cv2.VideoCapture(path) + self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) + + def __len__(self): + return self.nf # number of files + + +class LoadWebcam: # for inference + def __init__(self, pipe=0, img_size=640): + self.img_size = img_size + + if pipe == '0': + pipe = 0 # 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 + + def __iter__(self): + self.count = -1 + return self + + def __next__(self): + self.count += 1 + if cv2.waitKey(1) == ord('q'): # q to quit + self.cap.release() + cv2.destroyAllWindows() + raise StopIteration + + # Read frame + if self.pipe == 0: # local camera + ret_val, img0 = self.cap.read() + img0 = cv2.flip(img0, 1) # flip left-right + else: # IP camera + n = 0 + while True: + n += 1 + self.cap.grab() + if n % 30 == 0: # skip frames + ret_val, img0 = self.cap.retrieve() + if ret_val: + break + + # Print + assert ret_val, 'Camera Error %s' % self.pipe + img_path = 'webcam.jpg' + print('webcam %g: ' % self.count, end='') + + # Padded resize + img = letterbox(img0, new_shape=self.img_size)[0] + + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + + return img_path, img, img0, None + + def __len__(self): + return 0 + + +class LoadStreams: # multiple IP or RTSP cameras + def __init__(self, sources='streams.txt', img_size=640): + self.mode = 'images' + self.img_size = img_size + + if os.path.isfile(sources): + with open(sources, 'r') as f: + sources = [x.strip() for x in f.read().splitlines() if len(x.strip())] + else: + sources = [sources] + + n = len(sources) + self.imgs = [None] * n + self.sources = sources + 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(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)) + fps = cap.get(cv2.CAP_PROP_FPS) % 100 + _, self.imgs[i] = cap.read() # guarantee first frame + thread = Thread(target=self.update, args=([i, cap]), daemon=True) + print(' success (%gx%g at %.2f FPS).' % (w, h, fps)) + thread.start() + print('') # newline + + # check for common shapes + s = np.stack([letterbox(x, new_shape=self.img_size)[0].shape for x in self.imgs], 0) # inference shapes + self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal + if not self.rect: + print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.') + + def update(self, index, cap): + # Read next stream frame in a daemon thread + n = 0 + while cap.isOpened(): + n += 1 + # _, self.imgs[index] = cap.read() + cap.grab() + if n == 4: # read every 4th frame + _, self.imgs[index] = cap.retrieve() + n = 0 + time.sleep(0.01) # wait time + + def __iter__(self): + self.count = -1 + return self + + def __next__(self): + self.count += 1 + img0 = self.imgs.copy() + if cv2.waitKey(1) == ord('q'): # q to quit + cv2.destroyAllWindows() + raise StopIteration + + # Letterbox + img = [letterbox(x, new_shape=self.img_size, auto=self.rect)[0] for x in img0] + + # Stack + img = np.stack(img, 0) + + # Convert + img = img[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB, to bsx3x416x416 + img = np.ascontiguousarray(img) + + return self.sources, img, img0, None + + def __len__(self): + return 0 # 1E12 frames = 32 streams at 30 FPS for 30 years + +def rotate_augment(angle, scale, image, labels): + """ + 旋转目标增强 随机旋转 + @param angle: 旋转增强角度 int 单位为度 + @param scale: 设为1,尺度由train.py中定义 + @param image: img信息 shape(heght, width, 3) + @param labels: (num, [classid x_c y_c longside shortside Θ]) Θ ∈ int[0,180) + @return: + array rotated_img: augmented_img信息 shape(heght, width, 3) + array rotated_labels: augmented_label: (num, [classid x_c y_c longside shortside Θ]) + """ + Pi_angle = -angle * math.pi / 180.0 # 弧度制，后面旋转坐标需要用到，注意负号！！！ + rows, cols = image.shape[:2] + a, b = cols / 2, rows / 2 + M = cv2.getRotationMatrix2D(center=(a, b), angle=angle, scale=scale) + rotated_img = cv2.warpAffine(image, M, (cols, rows)) # 旋转后的图像保持大小不变 + rotated_labels = [] + for label in labels: + # rect=[(x_c,y_c),(w,h),Θ] Θ:flaot[0-179] -> (-180,0) + rect = longsideformat2cvminAreaRect(label[1], label[2], label[3], label[4], (label[5] - 179.9)) + # poly = [(x1,y1),(x2,y2),(x3,y3),(x4,y4)] + poly = cv2.boxPoints(rect) # 返回rect对应的四个点的值 normalized + + # 四点坐标反归一化 + poly[:, 0] = poly[:, 0] * cols + poly[:, 1] = poly[:, 1] * rows + + # 下面是计算旋转后目标相对旋转过后的图像的位置 + X0 = (poly[0][0] - a) * math.cos(Pi_angle) - (poly[0][1] - b) * math.sin(Pi_angle) + a + Y0 = (poly[0][0] - a) * math.sin(Pi_angle) + (poly[0][1] - b) * math.cos(Pi_angle) + b + + X1 = (poly[1][0] - a) * math.cos(Pi_angle) - (poly[1][1] - b) * math.sin(Pi_angle) + a + Y1 = (poly[1][0] - a) * math.sin(Pi_angle) + (poly[1][1] - b) * math.cos(Pi_angle) + b + + X2 = (poly[2][0] - a) * math.cos(Pi_angle) - (poly[2][1] - b) * math.sin(Pi_angle) + a + Y2 = (poly[2][0] - a) * math.sin(Pi_angle) + (poly[2][1] - b) * math.cos(Pi_angle) + b + + X3 = (poly[3][0] - a) * math.cos(Pi_angle) - (poly[3][1] - b) * math.sin(Pi_angle) + a + Y3 = (poly[3][0] - a) * math.sin(Pi_angle) + (poly[3][1] - b) * math.cos(Pi_angle) + b + + poly_rotated = np.array([(X0, Y0), (X1, Y1), (X2, Y2), (X3, Y3)]) + # 四点坐标归一化 + poly_rotated[:, 0] = poly_rotated[:, 0] / cols + poly_rotated[:, 1] = poly_rotated[:, 1] / rows + + rect_rotated = cv2.minAreaRect(np.float32(poly_rotated)) # 得到最小外接矩形的（中心(x,y), (宽,高), 旋转角度） + + c_x = rect_rotated[0][0] + c_y = rect_rotated[0][1] + w = rect_rotated[1][0] + h = rect_rotated[1][1] + theta = rect_rotated[-1] # Range for angle is [-90，0) + # (num, [classid x_c y_c longside shortside Θ]) + label[1:] = cvminAreaRect2longsideformat(c_x, c_y, w, h, theta) + + if (sum(label[1:-1] <= 0) + sum(label[1:3] >= 1)) >= 1: # 0= 1的元素,bbox中有<= 0的元素,已将某个box排除,') + np.clip(label[1:-1], 0, 1, out=label[1:-1]) + + label[-1] = int(label[-1] + 180.5) # range int[0,180] 四舍五入 + if label[-1] == 180: # range int[0,179] + label[-1] = 179 + rotated_labels.append(label) + + return rotated_img, np.array(rotated_labels) + +class LoadImagesAndLabels(Dataset): + """ + for training/testing + Args: + path: train_path or test_path eg：../coco128/images/train2017/ + img_size，batch_size，augment，hyp，rect，image_weights，cache_images，single_cls，stride，pad，rank + return: + class Dataset: + self.img_files 路径文件夹下所有图片路径 self.img_files=['??\\images\\train2017\\1.jpg',...,] + self.label_files 路径文件夹下所有label_txt路径 self.label_files=['??\\labels\\train2017\\1.txt',...,] + self.n 路径文件夹下所有图片的总数量 + self.batch , self.img_size , self.augment , self.hyp , self.image_weights , self.rect , self.mosaic , self.mosaic_border , self.stride , + self.shapes [[1.jpg的形状]...[n.jpg的形状]] eg：[[480 80][360 640]...[480 640]] + self.labels [array( [对应1.txt的labels信息] ，dtype=float32), ..., array( [对应n.txt的labels信息] ，dtype=float32)] + """ + 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): + try: + f = [] # image files + for p in path if isinstance(path, list) else [path]: + p = str(Path(p)) # os-agnostic + # 举例：parent = ‘..\coco128\images’ + '\' + parent = str(Path(p).parent) + os.sep + if os.path.isfile(p): # file + with open(p, 'r') as t: + t = t.read().splitlines() + f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path + elif os.path.isdir(p): # folder + f += glob.iglob(p + os.sep + '*.*') + else: + raise Exception('%s does not exist' % p) + # p路径文件夹下所有图片路径都会存在self.img_files中 self.img_files=['??\\images\\train2017\\1.jpg',...,] + self.img_files = sorted( + [x.replace('/', os.sep) for x in f if os.path.splitext(x)[-1].lower() in img_formats]) + except Exception as e: + raise Exception('Error loading data from %s: %s\nSee %s' % (path, e, 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 + 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 + sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep # sa=/images/, sb=/labels/ as substrings + # p路径文件夹下所有label_txt都会存在self.label_files self.label_files=['??\\labels\\train2017\\1.txt',...,] + self.label_files = [x.replace(sa, sb, 1).replace(os.path.splitext(x)[-1], '.txt') for x in self.img_files] + + # Check cache + # 初始化图片与标签，为缓存图片、标签做准备 + ''' + 创建缓存文件cache + List cache： { + '??\\images\\train2017\\1.jpg':[array( [对应1.txt的labels信息] ，dtype=float32), (weights, heights))] , + ... + '??\\images\\train2017\\n.jpg':[array( [对应n.txt的labels信息] ，dtype=float32), (weights, heights))] + } + ''' + cache_path = str(Path(self.label_files[0]).parent) + '.cache' # cached labels + if os.path.isfile(cache_path): + cache = torch.load(cache_path) # load + if cache['hash'] != get_hash(self.label_files + self.img_files): # dataset changed + cache = self.cache_labels(cache_path) # re-cache + else: + cache = self.cache_labels(cache_path) # cache + + # Get labels + ''' + self.shapes = [[1.jpg的形状]...[n.jpg的形状]] + self.labels = [array( [对应1.txt的labels信息] ，dtype=float32), ..., array( [对应n.txt的labels信息] ，dtype=float32)] + ''' + labels, shapes = zip(*[cache[x] for x in self.img_files]) + self.shapes = np.array(shapes, dtype=np.float64) + self.labels = list(labels) + + # Rectangular Training https://github.com/ultralytics/yolov3/issues/232 + if self.rect: + # Sort by aspect ratio 按纵横比的数值从小到大重新进行排序，矩形训练通常以成批处理 + s = self.shapes # wh + ar = s[:, 1] / s[:, 0] # aspect ratio + 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] + + # Set training image shapes + shapes = [[1, 1]] * nb + for i in range(nb): + ari = ar[bi == i] + mini, maxi = ari.min(), ari.max() + if maxi < 1: + shapes[i] = [maxi, 1] + elif mini > 1: + shapes[i] = [1, 1 / mini] + + self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride + + # Cache labels + 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 + ''' + self.label_files 路径文件夹下所有label_txt路径 self.label_files=['??\\labels\\train2017\\1.txt',...,] + self.labels = [array( [对应1.txt的labels信息] ,dtype=float32), ..., array( [对应n.txt的labels信息] ,dtype=float32)] + ''' + pbar = enumerate(self.label_files) + if rank in [-1, 0]: + pbar = tqdm(pbar) + for i, file in pbar: + l = self.labels[i] # label 第i张image的labels信息 size = (目标数量, [class, xywh_center(归一化),Θ]) + if l is not None and l.shape[0]: + # 判断标签是否有6列 [class ,xywh, Θ] + assert l.shape[1] == 6, '> 6 label columns: %s' % file + # 判断标签是否全部>=0 + assert (l >= 0).all(), 'negative labels: %s' % file + # 判断标签坐标x y 是否归一化 + assert (l[:, 1:3] <= 1).all(), 'non-normalized or out of bounds coordinate labels: %s' % file + # 找出标签中重复的坐标 + if np.unique(l, axis=0).shape[0] < l.shape[0]: # duplicate rows 若有重复目标则nd自增 + nd += 1 # print('WARNING: duplicate rows in %s' % self.label_files[i]) # duplicate rows + # 如果数据集只有一个类，设置类别标签为0 + 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 + # 获取目标框与图片，并将框从图片截取下来保存到本地(默认不使用) + if extract_bounding_boxes: + p = Path(self.img_files[i]) # 第i张image的path + img = cv2.imread(str(p)) + h, w = img.shape[:2] + for j, x in enumerate(l): # l ： label 第i张image的labels信息 size = (目标数量, [class, xywh]) + 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 + + # 对归一化的坐标乘以w，h + # x.size = [class ,xywh] + b = x[1:] * [w, h, w, h] # box + b[2:] = b[2:].max() # rectangle to square + b[2:] = b[2:] * 1.3 + 30 # pad + # xywh格式转xyxy + 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 + + if rank in [-1, 0]: + pbar.desc = 'Scanning labels %s (%g found, %g missing, %g empty, %g duplicate, for %g images)' % ( + cache_path, nf, nm, ne, nd, n) + if nf == 0: + s = 'WARNING: No labels found in %s. See %s' % (os.path.dirname(file) + os.sep, help_url) + print(s) + assert not augment, '%s. Can not train without labels.' % s + + # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM) + # 提前缓存图片到内存中，可以提升训练速度 + 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 + gb += self.imgs[i].nbytes + pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9) + + def cache_labels(self, path='labels.cache'): + ''' + Cache dataset labels, check images and read shapes + ''' + x = {} # dict + pbar = tqdm(zip(self.img_files, self.label_files), desc='Scanning images', total=len(self.img_files)) + for (img, label) in pbar: + try: + l = [] + image = Image.open(img) + image.verify() # PIL verify + # _ = io.imread(img) # skimage verify (from skimage import io) + shape = exif_size(image) # image size + assert (shape[0] > 9) & (shape[1] > 9), 'image size <10 pixels' + if os.path.isfile(label): + with open(label, 'r') as f: + l = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32) # labels + if len(l) == 0: # 当labels文件中内容为空时也要确保shape一致 + l = np.zeros((0, 6), dtype=np.float32) + x[img] = [l, shape] + except Exception as e: + x[img] = [None, None] + print('WARNING: %s: %s' % (img, e)) + + x['hash'] = get_hash(self.label_files + self.img_files) + torch.save(x, path) # save for next time + return x + + def __len__(self): + return len(self.img_files) + + # def __iter__(self): + # self.count = -1 + # print('ran dataset iter') + # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF) + # return self + + def __getitem__(self, index): # 只要实例对象（假定为p）做p[i]运算时，就会调用类中的__getitem__方法 + ''' + return torch.from_numpy(img), labels_out, self.img_files[index], shapes + @param index: dataset类的索引,只要调用实例对象（假定为p）做p[i]运算时，就会调用__getitem__方法 + @return: + img: 经预处理后的img;size = [3, resized_height, resized_width] + labels_out : (目标数量, [0, classid,归一化后的xywh,Θ]) + self.img_files[index] : 图片索引index的文件路径 + shapes： + ''' + if self.image_weights: + index = self.indices[index] + + hyp = self.hyp + mosaic = self.mosaic and random.random() < hyp['mosaic'] + if mosaic: + # Load mosaic + # img4 : size = (3 , size1, size2); + # labels : size = (单张img4中的目标GT数量, [classid ,LT_x,LT_y,RB_x,RB_y,Θ]); + img, labels = load_mosaic(self, index) + shapes = None + + # MixUp https://arxiv.org/pdf/1710.09412.pdf 对mosaic处理后的图片再一次进行随机mixup处理 + if random.random() < hyp['mixup']: + img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 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 + # 加载图片并根据设定的输入大小与图片原大小的比例ratio进行resize(未做填充pad到正方形) + img, (h0, w0), (h, w) = load_image(self, index) + + # Letterbox + # 如果进行矩形训练，则获取每个batch的输入图片的shape + shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape + img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) + shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling + + # Load labels + labels = [] + # self.labels = [array( [对应1.txt的labels信息] ，dtype=float32), ..., array( [对应n.txt的labels信息] ，dtype=float32)] + x = self.labels[index] # x.size = (目标数量, [class, xywh, Θ]) + if x.size > 0: + # Normalized xywh to pixel xyxy format + # 根据pad调整框的标签坐标，并从归一化的xywh->未归一化的xyxy + labels = x.copy() # labels.size = (单张图片中的目标数量, [class, xyxy]) + labels[:, 1] = ratio[0] * w * (x[:, 1] - x[:, 3] / 2) + pad[0] # pad width + labels[:, 2] = ratio[1] * h * (x[:, 2] - x[:, 4] / 2) + pad[1] # pad height + labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + pad[0] + labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1] + + if self.augment: + # Augment imagespace + if not mosaic: + # 随机对图片进行旋转，平移，缩放，裁剪 + img, labels = random_perspective(img, labels, + degrees=hyp['degrees'], + translate=hyp['translate'], + scale=hyp['scale'], + shear=hyp['shear'], + perspective=hyp['perspective']) + + # Augment colorspace + # 随机改变图片的色调（H），饱和度（S），亮度（V） + augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) + + # Apply cutouts + # if random.random() < 0.9: + # labels = cutout(img, labels) + + # labels.size = (目标数量, [class, xyxy, Θ]) + nL = len(labels) # number of labels + if nL: + # 调整框的标签，xyxy to xywh + labels[:, 1:5] = xyxy2xywh(labels[:, 1:5]) # convert xyxy to xywh + + # 重新归一化标签0 - 1 + labels[:, [2, 4]] /= img.shape[0] # normalized height 0-1 + labels[:, [1, 3]] /= img.shape[1] # normalized width 0-1 + + # labels.size = (目标数量, [class, xywh, Θ]) + if self.augment: + # flip up-down 上下翻转 沿x轴翻转 （y变 x不变） + if random.random() < hyp['flipud']: + img = np.flipud(img) + if nL: + labels[:, 2] = 1 - labels[:, 2] # y变x不变 + labels[:, -1] = 180 - labels[:, -1] # θ根据左右偏转也进行改变 + labels[labels[:, -1] == 180, -1] = 0 # 原θ=0时，情况特殊不做改变 + + # flip left-right 左右翻转 沿y轴翻转（y不变 x变） + if random.random() < hyp['fliplr']: + img = np.fliplr(img) + if nL: + labels[:, 1] = 1 - labels[:, 1] # x变y不变 + labels[:, -1] = 180 - labels[:, -1] # θ根据左右偏转也进行改变 + labels[labels[:, -1] == 180, -1] = 0 # 原θ=0时，情况特殊不做改变 + + # # 旋转augment + # if nL: + # degrees = 10.0 + # rotate_angle = random.uniform(-degrees, degrees) + # img, labels = rotate_augment(rotate_angle, 1, img, labels) + + # 初始化标签框对应的图片序号，配合下面的collate_fn使用 + labels_out = torch.zeros((nL, 7)) + if nL: + # labels.size=(目标数量, [class,xywh,Θ]) -> labels_out.size=(目标数量, [?, class,xywh,Θ]) + labels_out[:, 1:] = torch.from_numpy(labels) + # Convert + # img.size=[resized_height,resized_width,3] -> [3, resized_height, resized_width] + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + + ''' + img: 经预处理后的img size= [3, resized_height, resized_width] + labels_out : (目标数量, [0, classid,归一化后的xywh,Θ]) + self.img_files[index] : 图片索引index的文件路径 + shapes：不进行mosaic时进行矩形训练时才有值 + ''' + return torch.from_numpy(img), labels_out, self.img_files[index], shapes + + @staticmethod + def collate_fn(batch): # 取样器取样本的函数 即可通过该函数重写并自定义聚合为batch的方式 + """ + return img, labels, path, shapes + @param batch: 一个batch里面包含img，label，path，shapes 重写batch取样函数 + @return: + img : size = (batch_size, 3 , resized_height, resized_width) 没有归一化 + labels : size = (batch中的目标数量, [图片在当前batch中的索引,classid,归一化后的xywh, Θ]) + eg:[[0, 6, 0.5, 0.5, 0.26, 0.35, 179], + [0, 6, 0.5, 0.5, 0.26, 0.35, 179], + [1, 6, 0.5, 0.5, 0.26, 0.35, 179], + [2, 6, 0.5, 0.5, 0.26, 0.35, 179],] + path： 该batch中所有image的路径 size=batch_size + shapes: 该batch中所有image的shapes size=batch_size 不进行mosaic时进行矩形训练时才有值 + """ + # 一个batch中的img，标签信息，路径信息，形状信息 batch中的每个索引都由__getitem__函数提供 + # eg: label:[[1.txt的labels信息], ... ,[2.txt的labels信息]] + img, label, path, shapes = zip(*batch) # transposed + for i, l in enumerate(label): # i对应一个batch中的图片索引 + l[:, 0] = i # add target image index for build_targets() + # stack 和cat都是对tensor沿指定维度拼接，stack会增加一个维度，cat不会增加维度 + # img增加一个batch_size维度 + # label打破一个维度由label:[[1.txt的labels信息], ... ,[2.txt的labels信息]] -> [batch中的目标数量,[图片在当前batch中的索引,classid,归一化后的xywh]] + return torch.stack(img, 0), torch.cat(label, 0), path, shapes + + +# Ancillary functions -------------------------------------------------------------------------------------------------- +def load_image(self, index): + ''' + loads 1 image from dataset 加载训练列表中的一张图片 + @param self: dataset类 + @param index: 用于索引当前训练集中的图片 + @return: + ---------------------------------------- + 若图片无缓存： + img： 图像像素矩阵 size=(height, width, 3) + (h0, w0)： 图像原始的（height，width） + img.shape[:2]： 图像resize之后的（height，width） + 否则： + self.imgs[index]： 图像像素矩阵 size=(height, width, 3) + self.img_hw0[index]： 图像原始的（height，width） + self.img_hw[index]： 图像resize之后的（height，width） + ---------------------------------------- + ''' + img = self.imgs[index] + if img is None: # not cached + path = self.img_files[index] + img = cv2.imread(path) # BGR + assert img is not None, 'Image Not Found ' + path + h0, w0 = img.shape[:2] # orig hw + r = self.img_size / max(h0, w0) # resize image to img_size + if r != 1: # always resize down, only resize up if training with augmentation + interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR + img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp) + return img, (h0, w0), img.shape[:2] # img, hw_original, hw_resized + else: + return self.imgs[index], self.img_hw0[index], self.img_hw[index] # img, hw_original, hw_resized + + +def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5): + r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains + hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV)) + dtype = img.dtype # uint8 + + x = np.arange(0, 256, dtype=np.int16) + lut_hue = ((x * r[0]) % 180).astype(dtype) + lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) + lut_val = np.clip(x * r[2], 0, 255).astype(dtype) + + img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype) + cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed + + # Histogram equalization + # if random.random() < 0.2: + # for i in range(3): + # img[:, :, i] = cv2.equalizeHist(img[:, :, i]) + + +def load_mosaic(self, index): + ''' + loads 4 images in a mosaic + @param self: 一个dataset类 + @param index: 索引号，用于索引整个训练集合中的图片 + @return: + ——img4 : size = (resized_height,resized_ width, 3);经 + ——labels4 : size = (单张img4中的目标GT数量, [classid ,LT_x,LT_y,RB_x,RB_y,Θ]未归一化); + ''' + + labels4 = [] + s = self.img_size + # 随机取mosaic中心点 + yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y + # 随机取其他三张图片的索引 + indices = [index] + [random.randint(0, len(self.labels) - 1) for _ in range(3)] # 3 additional image indices + for i, index in enumerate(indices): + # Load image + # img.size = [resized_height,resized_ width, 3] + img, _, (h, w) = load_image(self, index) + + # place img in img4 + if i == 0: # top left + img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) 用于确定原图片在img4左上角的坐标（左上右下） + x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) 用于确定原图片剪裁进img4中的图像内容范围 + elif i == 1: # top right + x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc + x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h + elif i == 2: # bottom left + x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) + x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) + elif i == 3: # bottom right + x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) + x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) + + # img4.size = [resized_height,resized_ width, 3] + img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + padw = x1a - x1b # 原图片未剪裁进img4中的宽度 + padh = y1a - y1b # 原图片未剪裁进img4中的高度 + + # Labels + # self.labels[array([对应1.txt的labels信息] ，dtype = float32), ..., array([对应n.txt的labels信息] ，dtype = float32)] + x = self.labels[index] + labels = x.copy() + if x.size > 0: # Normalized xywh to pixel xyxy format 归一化的xywh转为非归一化的xyxy（左上右下）坐标形式 + labels[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padw # Left_top_x + labels[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + padh # Left_top_y + labels[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padw # right_bottom_x + labels[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + padh # right_bottom_y + labels4.append(labels) # labels4：[array([对应1.txt的labels信息 size=[n1,6]], ... ,array([对应4.txt的labels信息] size=[n4,6]] + + # Concat/clip labels + if len(labels4): + # labels4：[array([对应1.txt的labels信息 size=[n1,6]], ... ,array([对应4.txt的labels信息] size=[n4,6]] -> [4张图片的gt总数n1+n2+n3+n4,6] + # 即labels4.shape=(一张mosaic图片中的GT数量, [classid ,LT_x,LT_y,RB_x,RB_y,Θ]) + labels4 = np.concatenate(labels4, 0) # 将第一个维度取消 + np.clip(labels4[:, 1:5], 0, 2 * s, out=labels4[:, 1:5]) # 限定labels4[:, 1:5]中最小值只能为0，最大值只能为2*self.size + # img4, labels4 = replicate(img4, labels4) # replicate + + # Augment + 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 + + ''' + img4 : (size1, size2, 3) + labels4 : (单张img4中的目标GT数量, [classid ,LT_x,LT_y,RB_x,RB_y,Θ]) + ''' + return img4, labels4 + + +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 + @param new_shape: 矩形训练后的输出size + @param color: 用于填充图片未覆盖区域的背景色 + @return: + @param img: 待矩形训练后的输入图像 + @return: + img ： 矩形训练后的输出图像 + ratio ： [width_ratio , height_ratio] 最终size/原始size + (dw, dh) ：最小的左右/上下填充大小 + ''' + shape = img.shape[:2] # current shape [height, width] + if isinstance(new_shape, int): + new_shape = (new_shape, new_shape) + + # Scale ratio (new / old) + # 计算缩放因子 + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + if not scaleup: # only scale down, do not scale up (for better test mAP) + r = min(r, 1.0) + + # Compute padding + ratio = r, r # width, height ratios + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding + # 获取最小的矩形填充 + if auto: # minimum rectangle + dw, dh = np.mod(dw, 64), np.mod(dh, 64) # wh padding + elif scaleFill: # stretch + dw, dh = 0.0, 0.0 + new_unpad = (new_shape[1], new_shape[0]) + ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios + + # 计算上下左右填充大小 + dw /= 2 # divide padding into 2 sides + dh /= 2 + + if shape[::-1] != new_unpad: # resize + img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + # 进行填充 + img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border + return img, ratio, (dw, dh) + + +def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, border=(0, 0)): + ''' + 遍性数据增强： + 进行随机旋转，缩放，错切，平移，center，perspective数据增强 + Args: + img: shape=(height, width, 3) + targets ：size = (单张图片中的目标数量, [class, xyxy, Θ]) + Returns: + img：shape=(height, width, 3) + targets = (目标数量, [cls, xyxy, Θ]) + ''' + + 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) + + # 设置旋转和缩放的仿射矩阵并进行旋转和缩放 + # Rotation and Scale + R = np.eye(3) # 行数为3,对角线为1,其余为0的矩阵 + a = random.uniform(-degrees, degrees) # 随机生成[-degrees, degrees)的实数 即为旋转角度 负数则代表逆时针旋转 + # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations + s = random.uniform(1 - scale, 1 + scale) + # s = 2 ** random.uniform(-scale, scale) + R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) # 获得以(0,0)为中心的旋转仿射变化矩阵 + + # 设置裁剪的仿射矩阵系数 + # 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) + + # 设置平移的仿射系数 + # 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 + # 融合仿射矩阵并作用在图片上 + 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 + # 调整框的标签 + n = len(targets) # targets.size = (目标数量, [class, xyxy, Θ]) + if n: + # 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 # 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]] + y = xy[:, [1, 3, 5, 7]] + xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T + + # # apply angle-based reduction of bounding boxes + # radians = a * math.pi / 180 + # reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5 + # x = (xy[:, 2] + xy[:, 0]) / 2 + # y = (xy[:, 3] + xy[:, 1]) / 2 + # w = (xy[:, 2] - xy[:, 0]) * reduction + # 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 + + # clip boxes + xy_ = xy.copy() + xy_[:, [0, 2]] = xy[:, [0, 2]].clip(0, width) + xy_[:, [1, 3]] = xy[:, [1, 3]].clip(0, height) + + # 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): + # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 + h, w = image.shape[:2] + + def bbox_ioa(box1, box2): + # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2 + box2 = box2.transpose() + + # Get the coordinates of bounding boxes + 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] + + # Intersection area + inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \ + (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0) + + # box2 area + box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16 + + # Intersection over box2 area + return inter_area / box2_area + + # create random masks + scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction + for s in scales: + mask_h = random.randint(1, int(h * s)) + mask_w = random.randint(1, int(w * s)) + + # box + xmin = max(0, random.randint(0, w) - mask_w // 2) + ymin = max(0, random.randint(0, h) - mask_h // 2) + xmax = min(w, xmin + mask_w) + ymax = min(h, ymin + mask_h) + + # apply random color mask + image[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] + + # return unobscured labels + if len(labels) and s > 0.03: + box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) + ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area + labels = labels[ioa < 0.60] # remove >60% obscured labels + + return labels + + +def reduce_img_size(path='path/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 recursive_dataset2bmp(dataset='path/dataset_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='path/images.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'): + # Create folder + if os.path.exists(path): + shutil.rmtree(path) # delete output folder + os.makedirs(path) # make new output folder diff --git a/utils/evaluation_utils.py b/utils/evaluation_utils.py new file mode 100644 index 00000000..51016f69 --- /dev/null +++ b/utils/evaluation_utils.py @@ -0,0 +1,398 @@ +import torch +from utils.general import longsideformat2cvminAreaRect +import cv2 +import os +# -*- coding: utf-8 -*- +""" + To use the code, users should to config detpath, annopath and imagesetfile + detpath is the path for 15 result files, for the format, you can refer to "http://captain.whu.edu.cn/DOTAweb/tasks.html" + search for PATH_TO_BE_CONFIGURED to config the paths + Note, the evaluation is on the large scale images +""" +import os +import numpy as np +import re +import time +from utils import polyiou +import copy +import cv2 +import random +from PIL import Image + +## the IoU thresh for nms when merge image +nms_thresh = 0.3 + +def py_cpu_nms_poly(dets, thresh): + """ + 任意四点poly nms.取出nms后的边框的索引 + @param dets: shape(detection_num, [poly, confidence1]) 原始图像中的检测出的目标数量 + @param thresh: + @return: + keep: 经nms后的目标边框的索引 + """ + scores = dets[:, 8] + polys = [] + areas = [] + for i in range(len(dets)): + tm_polygon = polyiou.VectorDouble([dets[i][0], dets[i][1], + dets[i][2], dets[i][3], + dets[i][4], dets[i][5], + dets[i][6], dets[i][7]]) + polys.append(tm_polygon) + + # argsort将元素小到大排列 返回索引值 [::-1]即从后向前取元素 + order = scores.argsort()[::-1] # 取出元素的索引值 顺序为从大到小 + keep = [] + while order.size > 0: + ovr = [] + i = order[0] # 取出当前剩余置信度最大的目标边框的索引 + keep.append(i) + for j in range(order.size - 1): # 求出置信度最大poly与其他所有poly的IoU + iou = polyiou.iou_poly(polys[i], polys[order[j + 1]]) + ovr.append(iou) + ovr = np.array(ovr) + inds = np.where(ovr <= thresh)[0] # 找出iou小于阈值的索引 + order = order[inds + 1] + return keep + +def py_cpu_nms(dets, thresh): + """Pure Python NMS baseline.""" + #print('dets:', dets) + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scores = dets[:, 4] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + ## index for dets + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return keep + +def nmsbynamedict(nameboxdict, nameboxdict_classname, nms, thresh): + """ + 对namedict中的目标信息进行nms.不改变输入的数据形式 + @param nameboxdict: eg:{ + 'P706':[[poly1, confidence1], ..., [poly9, confidence9]], + ... + 'P700':[[poly1, confidence1], ..., [poly9, confidence9]] + } + @param nameboxdict_classname: eg:{ + 'P706':[[poly1, confidence1,'classname'], ..., [poly9, confidence9, 'classname']], + ... + 'P700':[[poly1, confidence1, 'classname'], ..., [poly9, confidence9, 'classname']] + } + @param nms: + @param thresh: nms阈值, IoU阈值 + @return: + nameboxnmsdict: eg:{ + 'P706':[[poly1, confidence1, 'classname'], ..., [poly_nms, confidence9, 'classname']], + ... + 'P700':[[poly1, confidence1, 'classname'], ..., [poly_nms, confidence9, 'classname']] + } + """ + # 初始化字典 + nameboxnmsdict = {x: [] for x in nameboxdict} # eg: nameboxnmsdict={'P0770': [], 'P1888': []} + for imgname in nameboxdict: # 提取nameboxdict中的key eg:P0770 P1888 + keep = nms(np.array(nameboxdict[imgname]), thresh) # rotated_nms索引值列表 + outdets = [] + #print('nameboxdict[imgname]: ', nameboxnmsdict[imgname]) + for index in keep: + # print('index:', index) + outdets.append(nameboxdict_classname[imgname][index]) + nameboxnmsdict[imgname] = outdets + return nameboxnmsdict + +def poly2origpoly(poly, x, y, rate): + origpoly = [] + for i in range(int(len(poly)/2)): + tmp_x = float(poly[i * 2] + x) / float(rate) + tmp_y = float(poly[i * 2 + 1] + y) / float(rate) + origpoly.append(tmp_x) + origpoly.append(tmp_y) + return origpoly + +def custombasename(fullname): + return os.path.basename(os.path.splitext(fullname)[0]) + +def GetFileFromThisRootDir(dir,ext = None): + allfiles = [] + needExtFilter = (ext != None) + for root,dirs,files in os.walk(dir): + for filespath in files: + filepath = os.path.join(root, filespath) + extension = os.path.splitext(filepath)[1][1:] + if needExtFilter and extension in ext: + allfiles.append(filepath) + elif not needExtFilter: + allfiles.append(filepath) + return allfiles + +def mergebase(srcpath, dstpath, nms): + """ + 将源路径中所有的txt目标信息,经nms后存入目标路径中的同名txt + @param srcpath: 合并前信息保存的txt源路径 + @param dstpath: 合并后信息保存的txt目标路径 + @param nms: NMS函数 + """ + filelist = GetFileFromThisRootDir(srcpath) # srcpath文件夹下的所有文件相对路径 eg:['example_split/../P0001.txt', ..., '?.txt'] + for fullname in filelist: # 'example_split/../P0001.txt' + name = custombasename(fullname) # 只留下文件名 eg:P0001 + dstname = os.path.join(dstpath, name + '.txt') # eg: example_merge/..P0001.txt + if not os.path.exists(dstpath): + os.makedirs(dstpath) + with open(fullname, 'r') as f_in: + nameboxdict = {} + nameboxdict_classname = {} + lines = f_in.readlines() # 读取txt中所有行,每行作为一个元素存于list中 + splitlines = [x.strip().split(' ') for x in lines] # 再次分割list中的每行元素 shape:n行 * m个元素 + for splitline in splitlines: # splitline:每行中的m个元素 + # splitline = [待merge图片名(该目标所处图片名称), confidence, x1, y1, x2, y2, x3, y3, x4, y4, classname] + subname = splitline[0] # 每行的第一个元素 是被分割的图片的图片名 eg:P0706__1__0___0 + splitname = subname.split('__') # 分割待merge的图像的名称 eg:['P0706','1','0','_0'] + oriname = splitname[0] # 获得待merge图像的原图像名称 eg:P706 + pattern1 = re.compile(r'__\d+___\d+') # 预先编译好r'__\d+___\d+' 提高重复使用效率 \d表示数字 + + x_y = re.findall(pattern1, subname) # 匹配subname中的字符串 eg: x_y=['__0___0'] + x_y_2 = re.findall(r'\d+', x_y[0]) # 匹配subname中的字符串 eg: x_y_2= ['0','0'] + x, y = int(x_y_2[0]), int(x_y_2[1]) # 找到当前subname图片在原图中的分割位置 + + pattern2 = re.compile(r'__([\d+\.]+)__\d+___') # \.表示一切字符 + + rate = re.findall(pattern2, subname)[0] # 找到该subname分割图片时的分割rate (resize rate before cut) + + confidence = splitline[1] + classname = splitline[-1] + poly = list(map(float, splitline[2:10])) # 每个元素映射为浮点数 再放入列表中 + origpoly = poly2origpoly(poly, x, y, rate) # 将目标位置信息resize 恢复成原图的poly坐标 + det = origpoly # shape(8) + det.append(confidence) # [poly, 'confidence'] + det = list(map(float, det)) # [poly, confidence] + + det_classname = copy.deepcopy(det) + det_classname.append(classname) # [poly, 'confidence','classname'] + if (oriname not in nameboxdict): + nameboxdict[oriname] = [] # 弄个元组,汇集原图目标信息 eg: 'P706':[[poly1, confidence1], ..., ] + nameboxdict_classname[oriname] = [] # 弄个元组,汇集原图目标信息 eg: 'P706':[[poly1, confidence1,'classname'], ..., ] + nameboxdict[oriname].append(det) + nameboxdict_classname[oriname].append(det_classname) + + nameboxnmsdict = nmsbynamedict(nameboxdict, nameboxdict_classname, nms, nms_thresh) # 对nameboxdict元组进行nms + with open(dstname, 'w') as f_out: + for imgname in nameboxnmsdict: # 'P706' + for det in nameboxnmsdict[imgname]: # 取出对应图片的nms后的目标信息 + # det:[poly1, confidence1, 'classname'] + #print('det:', det) + confidence = det[-2] + bbox = det[0:-2] + outline = imgname + ' ' + str(confidence) + ' ' + ' '.join(map(str, bbox)) + ' ' + det[-1] + #print('outline:', outline) + f_out.write(outline + '\n') + +def mergebyrec(srcpath, dstpath): + """ + srcpath: result files before merge and nms + dstpath: result files after merge and nms + """ + # srcpath = r'E:\bod-dataset\results\bod-v3_rfcn_2000000' + # dstpath = r'E:\bod-dataset\results\bod-v3_rfcn_2000000_nms' + + mergebase(srcpath, + dstpath, + py_cpu_nms) +def mergebypoly(srcpath, dstpath): + """ + @param srcpath: result files before merge and nms.txt的信息格式为:[P0770__1__0___0 confidence poly 'classname'] + @param dstpath: result files after merge and nms.保存的txt信息格式为:[P0770 confidence poly 'classname'] + """ + # srcpath = r'/home/dingjian/evaluation_task1/result/faster-rcnn-59/comp4_test_results' + # dstpath = r'/home/dingjian/evaluation_task1/result/faster-rcnn-59/testtime' + + mergebase(srcpath, + dstpath, + py_cpu_nms_poly) + +def rbox2txt(rbox, classname, conf, img_name, out_path, pi_format=False): + """ + 将分割图片的目标信息填入原始图片.txt中 + @param robx: rbox:[tensor(x),tensor(y),tensor(l),tensor(s),tensor(θ)] + @param classname: string + @param conf: string + @param img_name: string + @param path: 文件夹路径 str + @param pi_format: θ是否为pi且 θ ∈ [-pi/2,pi/2) False说明 θ∈[0,179] + """ + if isinstance(rbox, torch.Tensor): + rbox = rbox.cpu().float().numpy() + + #rbox = np.array(x) + if pi_format: # θ∈[-pi/2,pi/2) + rbox[-1] = (rbox[-1] * 180 / np.pi) + 90 # θ∈[0,179] + + # rect=[(x_c,y_c),(w,h),Θ] Θ:flaot[0-179] -> (-180,0) + rect = longsideformat2cvminAreaRect(rbox[0], rbox[1], rbox[2], rbox[3], (rbox[4] - 179.9)) + # poly = [(x1,y1),(x2,y2),(x3,y3),(x4,y4)] + poly = np.float32(cv2.boxPoints(rect)) # 返回rect对应的四个点的值 + poly = np.int0(poly).reshape(8) + + splitname = img_name.split('__') # 分割待merge的图像的名称 eg:['P0706','1','0','_0'] + oriname = splitname[0] # 获得待merge图像的原图像名称 eg:P706 + + # 目标所属图片名称_分割id 置信度 poly classname + lines = img_name + ' ' + conf + ' ' + ' '.join(list(map(str, poly))) + ' ' + classname + # 移除之前的输出文件夹,并新建输出文件夹 + if not os.path.exists(out_path): + os.makedirs(out_path) # make new output folder + + with open(str(out_path + '/' + oriname) + '.txt', 'a') as f: + f.writelines(lines + '\n') + +def evaluation_trans(srcpath, dstpath): + """ + 将srcpath文件夹中的所有txt中的目标提取出来,按照目标类别分别存入 Task1_类别名.txt中: + txt中的内容格式: 目标所属原始图片名称 置信度 poly + @param srcpath: 存放图片的目标检测结果(文件夹,内含多个txt) + txt中的内容格式: 目标所属图片名称 置信度 poly 'classname' + @param dstpath: 存放图片的目标检测结果(文件夹, 内含多个Task1_类别名.txt ) + txt中的内容格式: 目标所属原始图片名称 置信度 poly + """ + filelist = GetFileFromThisRootDir(srcpath) # srcpath文件夹下的所有文件相对路径 eg:['result_merged/P0001.txt', ..., '?.txt'] + for fullname in filelist: # 'result_merged/P0001.txt' + if not os.path.exists(dstpath): + os.makedirs(dstpath) + with open(fullname, 'r') as f_in: + lines = f_in.readlines() # 读取txt中所有行,每行作为一个元素存于list中 + splitlines = [x.strip().split(' ') for x in lines] # 再次分割list中的每行元素 shape:n行 * m个元素 + for splitline in splitlines: # splitline:每行中的m个元素 + # splitline = [目标所属图片名称, confidence, x1, y1, x2, y2, x3, y3, x4, y4, 'classname'] + classname = splitline[-1] # 每行的最后一个元素 是被分割的图片的种类名 + dstname = os.path.join(dstpath, 'Task1_' + classname + '.txt') # eg: result/Task1_plane.txt + lines_ = ' '.join(list(splitline[:-1])) + with open(dstname, 'a') as f: + f.writelines(lines_ + '\n') + +def image2txt(srcpath, dstpath): + """ + 将srcpath文件夹下的所有子文件名称打印到namefile.txt中 + @param srcpath: imageset + @param dstpath: imgnamefile.txt的存放路径 + """ + filelist = GetFileFromThisRootDir(srcpath) # srcpath文件夹下的所有文件相对路径 eg:['example_split/../P0001.txt', ..., '?.txt'] + for fullname in filelist: # 'example_split/../P0001.txt' + name = custombasename(fullname) # 只留下文件名 eg:P0001 + dstname = os.path.join(dstpath, 'imgnamefile.txt') # eg: result/imgnamefile.txt + if not os.path.exists(dstpath): + os.makedirs(dstpath) + with open(dstname, 'a') as f: + f.writelines(name + '\n') + +def draw_DOTA_image(imgsrcpath, imglabelspath, dstpath, extractclassname, thickness): + """ + 绘制工具merge后的目标/DOTA GT图像 + @param imgsrcpath: merged后的图像路径(原始图像路径) + @param imglabelspath: merged后的labels路径 + @param dstpath: 目标绘制之后的保存路径 + @param extractclassname: the category you selected + """ + if not os.path.exists(dstpath): + os.makedirs(dstpath) + # 设置画框的颜色 colors = [[178, 63, 143], [25, 184, 176], [238, 152, 129],....,[235, 137, 120]]随机设置RGB颜色 + colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(extractclassname))] + filelist = GetFileFromThisRootDir(imglabelspath) # fileist=['/.../P0005.txt', ..., /.../P000?.txt] + for fullname in filelist: # fullname='/.../P000?.txt' + objects = [] + with open(fullname, 'r') as f_in: # 打开merge后/原始的DOTA图像的gt.txt + lines = f_in.readlines() # 读取txt中所有行,每行作为一个元素存于list中 + splitlines = [x.strip().split(' ') for x in lines] # 再次分割list中的每行元素 shape:n行 * m个元素 + if len(splitlines[0]) == 1: # 首行为"imagesource:GoogleEarth",说明为DOTA原始labels + # DOTA labels:[polys classname 1/0] + del splitlines[0] + del splitlines[0] # 删除前两个无用信息 + objects = [x[0:-2] for x in splitlines] + classnames = [x[-2] for x in splitlines] + else: + # P0003 0.911 660.0 309.0 639.0 209.0 661.0 204.0 682.0 304.0 large-vehicle + objects = [x[2:-1] for x in splitlines] + classnames = [x[-1] for x in splitlines] + + ''' + objects[i] = str[poly, classname] + ''' + name = os.path.splitext(os.path.basename(fullname))[0] # name='P000?' + img_fullname = os.path.join(imgsrcpath, name + '.png') + img_savename = os.path.join(dstpath, name + '_.png') + img = cv2.imread(img_fullname) # 读取图像像素 + + for i, obj in enumerate(objects): + # obj = [poly ,'classname'] + classname = classnames[i] + # poly = [(x1,y1),(x2,y2),(x3,y3),(x4,y4)] + poly = np.array(list(map(float, obj))) + poly = poly.reshape(4, 2) # 返回rect对应的四个点的值 normalized + poly = np.int0(poly) + + # 画出来 + cv2.drawContours(image=img, + contours=[poly], + contourIdx=-1, + color=colors[int(extractclassname.index(classname))], + thickness=thickness) + cv2.imwrite(img_savename, img) + + + + + + + +if __name__ == '__main__': + ''' + 计算AP的流程: + 1.detect.py检测一个文件夹的所有图片并把检测结果按照图片原始来源存入 原始图片名称.txt中: (rbox2txt函数) + txt中的内容格式: 目标所属图片名称_分割id 置信度 poly classname + 2.ResultMerge.py将所有 原始图片名称.txt 进行merge和nms,并将结果存入到另一个文件夹的 原始图片名称.txt中: (mergebypoly函数) + txt中的内容格式: 目标所属图片名称 置信度 poly classname + 3.写一个evaluation_trans函数将上个文件夹中的所有txt中的目标提取出来,按照目标类别分别存入 Task1_类别名.txt中: + txt中的内容格式: 目标所属原始图片名称 置信度 poly + 4.写一个imgname2txt.py 将测试集的所有图片名称打印到namefile.txt中 + ''' + # see demo for example + mergebypoly(r'/home/test/Persons/hukaixuan/yolov5_DOTA_OBB/DOTA_demo_view/detection/result_txt/result_before_merge', + r'/home/test/Persons/hukaixuan/yolov5_DOTA_OBB/DOTA_demo_view/detection/result_txt/result_merged') + print('检测结果已merge') + evaluation_trans(r'/home/test/Persons/hukaixuan/yolov5_DOTA_OBB/DOTA_demo_view/detection/result_txt/result_merged', + r'/home/test/Persons/hukaixuan/yolov5_DOTA_OBB/DOTA_demo_view/detection/result_txt/result_classname') + print('检测结果已按照类别分类') + image2txt(r'/home/test/Persons/hukaixuan/yolov5_DOTA_OBB/DOTA_demo_view/row_images', # val原图数据集路径 + r'/home/test/Persons/hukaixuan/yolov5_DOTA_OBB/DOTA_demo_view/detection/result_txt') + print('校验数据集名称文件已生成') + + # classnames_v1_5 = ['plane', 'baseball-diamond', 'bridge', 'ground-track-field', 'small-vehicle', 'large-vehicle', + # 'ship', 'tennis-court', + # 'basketball-court', 'storage-tank', 'soccer-ball-field', 'roundabout', 'harbor', 'swimming-pool', + # 'helicopter', 'container-crane'] + # + # draw_DOTA_image(imgsrcpath=r'/home/test/Persons/hukaixuan/yolov5_DOTA_OBB/DOTA_demo_view/row_images', + # imglabelspath=r'/home/test/Persons/hukaixuan/yolov5_DOTA_OBB/DOTA_demo_view/detection/result_txt/result_merged', + # dstpath=r'/home/test/Persons/hukaixuan/yolov5_DOTA_OBB/DOTA_demo_view/detection/merged_drawed', + # extractclassname=classnames_v1_5, + # thickness=2 + # ) \ No newline at end of file diff --git a/utils/evolve.sh b/utils/evolve.sh new file mode 100644 index 00000000..5de9f7a2 --- /dev/null +++ b/utils/evolve.sh @@ -0,0 +1,15 @@ +#!/bin/bash +# Hyperparameter evolution commands (avoids CUDA memory leakage issues) +# Replaces train.py python generations 'for' loop with a bash 'for' loop + +# Start on 4-GPU machine +#for i in 0 1 2 3; do +# t=ultralytics/yolov5:evolve && sudo docker pull $t && sudo docker run -d --ipc=host --gpus all -v "$(pwd)"/VOC:/usr/src/VOC $t bash utils/evolve.sh $i +# sleep 60 # avoid simultaneous evolve.txt read/write +#done + +# Hyperparameter evolution commands +while true; do + # python train.py --batch 64 --weights yolov5m.pt --data voc.yaml --img 512 --epochs 50 --evolve --bucket ult/evolve/voc --device $1 + python train.py --batch 40 --weights yolov5m.pt --data coco.yaml --img 640 --epochs 30 --evolve --bucket ult/evolve/coco --device $1 +done diff --git a/utils/general.py b/utils/general.py new file mode 100644 index 00000000..d224a6b8 --- /dev/null +++ b/utils/general.py @@ -0,0 +1,2085 @@ +import glob +import logging +import math +import os +import platform +import random +import shutil +import subprocess +import time +from contextlib import contextmanager +from copy import copy +from pathlib import Path + +import cv2 +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import torch +import torch.nn as nn +import yaml +from scipy.cluster.vq import kmeans +from scipy.signal import butter, filtfilt +from tqdm import tqdm + +from utils.google_utils import gsutil_getsize +from utils.torch_utils import is_parallel, init_torch_seeds +from shapely.geometry import Polygon, MultiPoint + +from utils import polyiou +import pdb + +# 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) + + +@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. + Decorator使分布式训练中的所有进程等待每个本地的主进程做一些事情。 + """ + if local_rank not in [-1, 0]: + torch.distributed.barrier() + yield + if local_rank == 0: + torch.distributed.barrier() + + +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): + ''' + 设置唯一确定随机数种子，确保随机数种子不变，使得程序每次使用random函数均可获得同一随机值,即确保神经网络每次初始化都相同 + ''' + random.seed(seed) + np.random.seed(seed) + init_torch_seeds(seed) + + +def get_latest_run(search_dir='./runs'): + ''' + 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 + return new_size + ''' + 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_anchors(dataset, model, thr=4.0, imgsz=640): + ''' + Check anchor fit to data, recompute if necessary + 利用预设值anchor基于shape规则对bbox计算best possible recall + 若召回率大于一定值，则不进行优化，直接返回 + 若召回率低，则利用遗传算法+kmeans重新计算anchor + ''' + 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 generate improved anchors, please wait...' % bpr) + 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 check_anchor_order(m): + ''' + # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary + # 检查YOLOv5 Detect（）模块m的anchor顺序和stride顺序，如有必要，进行纠正，确保anchor顺序是从小物体的anchor到大物体的anchor + @param m: Detect类 + ''' + # prod返回指定数轴上所有元素的乘积 view(-1)将数据展开为一维数组 + 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_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 + ''' + val, s = dict.get('val'), dict.get('download') + if val and len(val): + val = [os.path.abspath(x) for x in (val if isinstance(val, list) else [val])] # val path + if not all(os.path.exists(x) for x in val): + print('\nWARNING: Dataset not found, nonexistant paths: %s' % [*val]) + 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 divisble by divisor , 返回可被除数divisor整除的x,否则返回divisor + return math.ceil(x / divisor) * divisor + + +def labels_to_class_weights(labels, nc=80): + ''' + Get class weights (inverse frequency) from training labels 获取图像的采样权重（图像类别的反频率：图像类别频率高的采样频率低） + ''' + 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] classes : size=(866643) + weights = np.bincount(classes, minlength=nc) # occurences per class 输出长度为nc的数组，其中数值为每一类别出现的频数 + + # Prepend gridpoint count (for uCE trianing) + # 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 种类频数为0，则用1来填充 + weights = 1 / weights # number of targets per class 频数取反，频数越高，反而此时的数值越低（频率取反） + weights /= weights.sum() # normalize 求出每个类别的占总数的反比例 + 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 = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(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 = torch.zeros_like(x) if isinstance(x, torch.Tensor) else np.zeros_like(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_labels(img1_shape, labels, img0_shape, ratio_pad=None): + ''' + Rescale coords (xywh) from img1_shape to img0_shape + 将检测出的目标边框坐标从 img1_shape 形状放缩到 img0_shape，即反resize+pad，将目标边框对应至初始原图 + @param img1_shape: 原始形状 (height, width) + @param labels: (num ,[ x y longside shortside Θ]) + @param img0_shape: 目标形状 (height, width) + @param ratio_pad: + @return: + scaled_labels : (num ,[ x y longside shortside Θ]) + ''' + 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] + + scaled_labels = [] + for i, label in enumerate(labels): + # rect=[(x_c,y_c),(w,h),Θ] Θ:flaot[0-179] -> (-180,0) + rect = longsideformat2cvminAreaRect(label[0], label[1], label[2], label[3], (label[4] - 179.9)) + # poly = [(x1,y1),(x2,y2),(x3,y3),(x4,y4)] + poly = cv2.boxPoints(rect) # 返回rect对应的四个点的值 normalized + + poly[:, 0] -= pad[0] # x padding + poly[:, 1] -= pad[1] # y padding + poly[:, :] /= gain + clip_poly(poly, img0_shape) + + rect_scale = cv2.minAreaRect(np.float32(poly)) # 得到最小外接矩形的（中心(x,y), (宽,高), 旋转角度） + + c_x = rect_scale[0][0] + c_y = rect_scale[0][1] + w = rect_scale[1][0] + h = rect_scale[1][1] + theta = rect_scale[-1] # Range for angle is [-90，0) + + label = np.array(cvminAreaRect2longsideformat(c_x, c_y, w, h, theta)) + + label[-1] = int(label[-1] + 180.5) # range int[0,180] 四舍五入 + if label[-1] == 180: # range int[0,179] + label[-1] = 179 + scaled_labels.append(label) + + return torch.from_numpy(np.array(scaled_labels)) + + +def clip_poly(poly, img_shape): + ''' + Clip bounding [(x1,y1),(x2,y2),(x3,y3),(x4,y4)] bounding boxes to image shape (height, width) + ''' + poly[:, 0].clip(0, img_shape[1]) # x + poly[:, 1].clip(0, img_shape[0]) # y + +def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, fname='precision-recall_curve.png'): + """ 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 + fname: Plot filename + # 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_gt = (target_cls == c).sum() # Number of ground truth objects + n_p = i.sum() # Number of predicted objects + + if n_p == 0 or n_gt == 0: + continue + else: + # Accumulate FPs and TPs + fpc = (1 - tp[i]).cumsum(0) + tpc = tp[i].cumsum(0) + + # Recall + recall = tpc / (n_gt + 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 + py.append(np.interp(px, recall[:, 0], precision[:, 0])) # precision at mAP@0.5 + for j in range(tp.shape[1]): + ap[ci, j] = compute_ap(recall[:, j], precision[:, j]) + + # Compute F1 score (harmonic mean of precision and recall) + f1 = 2 * p * r / (p + r + 1e-16) + + if plot: + py = np.stack(py, axis=1) + fig, ax = plt.subplots(1, 1, figsize=(5, 5)) + ax.plot(px, py, linewidth=0.5, color='grey') # plot(recall, precision) + ax.plot(px, py.mean(1), linewidth=2, color='blue', label='all classes') + ax.set_xlabel('Recall') + ax.set_ylabel('Precision') + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + plt.legend() + fig.tight_layout() + fig.savefig(fname, dpi=200) + + 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 = np.concatenate(([0.], recall, [min(recall[-1] + 1E-3, 1.)])) + mpre = 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 + +# 计算旋转矩形iou +def rotate_box_iou(box1, box2, GIoU=False): + """ + 计算box1中的所有box 与 box2中的所有box的旋转矩形iou （1对1） + :param box1: GT tensor size=(n, [xywhθ]) + :param box2: anchor size= (n, [xywhθ]) + :param GIoU: 是否使用GIoU的标志位 + :return: + box2所有box与box1的IoU size= (n) + """ + ft = torch.cuda.FloatTensor + if isinstance(box1, list): box1 = ft(box1) + if isinstance(box2, list): box2 = ft(box2) + + if len(box1.shape) < len(box2.shape): # 输入的单box维度不匹配时，unsqueeze一下 确保两个维度对应两个维度 + box1 = box1.unsqueeze(0) + if len(box2.shape) < len(box1.shape): # 输入的单box维度不匹配时，unsqueeze一下 确保两个维度对应两个维度 + box2 = box2.unsqueeze(0) + if not box1.shape == box2.shape: # 若两者num数量不等则报错 + print('计算旋转矩形iou时有误，输入shape不相等') + print('----------------box1:--------------------') + print(box1.shape) + print(box1) + print('----------------box2:--------------------') + print(box2.shape) + print(box2) + # print(box1) + # box(n, [xywhθ]) + box1 = box1[:, :5] + box2 = box2[:, :5] + + if GIoU: + mode = 'giou' + else: + mode = 'iou' + + ious = [] + for i in range(len(box2)): + # print(i) + r_b1 = get_rotated_coors(box1[i]) + r_b2 = get_rotated_coors(box2[i]) + + ious.append(skewiou(r_b1, r_b2, mode=mode)) + + # if GIoU: # Generalized IoU https://arxiv.org/pdf/1902.09630.pdf + # c_x1, c_x2 = torch.min(b1_x1, b2_x1), torch.max(b1_x2, b2_x2) + # c_y1, c_y2 = torch.min(b1_y1, b2_y1), torch.max(b1_y2, b2_y2) + # c_area = (c_x2 - c_x1) * (c_y2 - c_y1) # convex area + # return iou - (c_area - union_area) / c_area # GIoU + # print(ious) + return ft(ious) + +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 4xn, box2 is nx4 + @param box1: shape([xy_offsets_feature,wh_feature], num) + @param box2: shape(num, [xy_offsets_feature,wh_feature]) + @param x1y1x2y2: bbox的表示形式是否已经是xyxy？ + @return: iou shape=(num, 1) + """ + 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 # eps防止分母变为0 + w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps + union = w1 * h1 + w2 * h2 - inter + eps + + iou = inter / union # IoU= (A∩B)/(A∪B) + if GIoU or DIoU or CIoU: + cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width 并集外接矩形的宽 + ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height 并集外接矩形的高 + if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 + c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared 并集最小外接矩形的对角线距离(squared) + rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center distance squared 两矩形中心点欧氏距离的平方 + if DIoU: + return iou - rho2 / c2 # DIoU = IoU - 惩罚项 + 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 = DIou - αv + else: # GIoU https://arxiv.org/pdf/1902.09630.pdf + c_area = cw * ch + eps # convex area 并集外接矩形的面积 + return iou - (c_area - union) / c_area # GIoU = IoU - (C-A∪B)/C + 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) + + +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 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() + +def gaussian_label(label, num_class, u=0, sig=4.0): + ''' + 转换成CSL Labels： + 用高斯窗口函数根据角度θ的周期性赋予gt labels同样的周期性，使得损失函数在计算边界处时可以做到“差值很大但loss很小”； + 并且使得其labels具有环形特征，能够反映各个θ之间的角度距离 + @param label: 当前box的θ类别 shape(1) + @param num_class: θ类别数量=180 + @param u: 高斯函数中的μ + @param sig: 高斯函数中的σ + @return: 高斯离散数组:将高斯函数的最高值设置在θ所在的位置，例如label为45，则将高斯分布数列向右移动直至x轴为45时，取值为1 shape(180) + ''' + # floor()返回数字的下舍整数 ceil() 函数返回数字的上入整数 range(-90,90) + # 以num_class=180为例，生成从-90到89的数字整形list shape(180) + x = np.array(range(math.floor(-num_class / 2), math.ceil(num_class / 2), 1)) + y_sig = np.exp(-(x - u) ** 2 / (2 * sig ** 2)) # shape(180) 为-90到89的经高斯公式计算后的数值 + # 将高斯函数的最高值设置在θ所在的位置，例如label为45，则将高斯分布数列向右移动直至x轴为45时，取值为1 + return np.concatenate([y_sig[math.ceil(num_class / 2) - int(label.item()):], + y_sig[:math.ceil(num_class / 2) - int(label.item())]], axis=0) + +def rbox_iou(box1, theta1, box2, theta2): + """ + compute rotated box IoU + @param box1: torch.size(num, 4) + @param theta1: torch.size(num, 1) + @param box2: torch.size(num, 4) + @param theta2: torch.size(num, 1) + @return: + rbox_iou numpy_array shape(num, 1) + """ + # theta2 = theta2.unsqueeze(1) # torch.size num -> (num,1) + polys1 = [] + polys2 = [] + rboxes1 = torch.cat((box1, theta1), 1) + rboxes2 = torch.cat((box2, theta2), 1) + for rbox1 in rboxes1: + poly = longsideformat2poly(rbox1[0], rbox1[1], rbox1[2], rbox1[3], rbox1[4]) + polys1.append(polyiou.VectorDouble(poly)) + for rbox2 in rboxes2: + poly = longsideformat2poly(rbox2[0], rbox2[1], rbox2[2], rbox2[3], rbox2[4]) + polys2.append(polyiou.VectorDouble(poly)) + IoUs = [] + for i in range(len(polys1)): + iou = polyiou.iou_poly(polys1[i], polys2[i]) + IoUs.append(iou) + IoUs = np.array(IoUs) + + return IoUs + + + +def compute_loss(p, targets, model, csl_label_flag=True): + ''' + @param p: [small_forward, medium_forward, large_forward] eg:small_forward.size=( batch_size, 3种scale框, size1, size2, no) + @param targets: torch.Size = (该batch中的目标数量, [该image属于该batch的第几个图片, class, xywh,Θ]) + @param model: 网络模型 + @param csl_label_flag: θ是否采用CSL_labels来计算分类损失 + @return: + loss * bs : 标量 ； + torch.cat((lbox, lobj, lcls, langle, loss)).detach() : 不参与网络更新的标量 list(边框损失, 置信度损失, 分类损失, 角度loss,总损失) + ''' + device = targets.device + # 初始化各个部分损失 + lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) + langle = torch.zeros(1, device=device) + # 获得标签分类，边框，索引，anchor + ''' + tcls : 3个tensor组成的list (tensor_class_list[i]) 对每个步长网络生成对应的class tensor + tcls[i].shape=(num_i, 1) + tbox : 3个tensor组成的list (box[i]) 对每个步长网络生成对应的gt_box信息 xy：当前featuremap尺度上的真实gt_xy与负责预测网格坐标的偏移量; wh：当前featuremap尺度上的真实gt_wh + tbox[i].shape=(num_i, 4) + indices : 索引列表 也由3个大list组成 每个list代表对每个步长网络生成的索引数据。其中单个list中的索引数据分别有: + (该image属于该batch的第几个图片 ; 该box属于哪种scale的anchor; 网格索引1; 网格索引2) + indices[i].shape=(4, num_i) + anchors : anchor列表 也由3个list组成 每个list代表每个步长网络对gt目标采用的anchor大小(对应featuremap尺度上的anchor_wh) + anchor[i].shape=(num_i, 2) + tangle : 3个tensor组成的list (tensor_angle_list[i]) 对每个步长网络生成对应的class tensor + tangle[i].shape=(num_i) + ''' + tcls, tbox, indices, anchors, tangle = build_targets(p, targets, model) # targets + h = model.hyp # hyperparameters + + # Define criteria + # 定义损失函数 分类损失和 置信度损失 + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['cls_pw']])).to(device) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['obj_pw']])).to(device) + BCEangle = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['angle_pw']])).to(device) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + # 标签平滑，eps默认为0，其实是没用上 cp = 1; cn = 0 + cp, cn = smooth_BCE(eps=0.0) + + # Focal loss + # 如果设置了fl_gamma参数，就使用focal loss，默认也是没使用的 + g = h['fl_gamma'] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + BCEangle = FocalLoss(BCEangle, g) + + # Losses + + nt = 0 # number of targets + np = len(p) # number of inference outputs = 3 + # 设置三个特征图对应输出的损失系数 4.0, 1.0, 0.4分别对应下采样8,16,32的输出层 + balance = [4.0, 1.0, 0.4] if np == 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 + # 根据indices获取索引，方便找到对应网格的输出 + # pi.size = (batch_size, 3种scale框, size1, size2, [xywh,score,num_classes,num_angles]) + # indice[i] = (该image属于该batch的第几个图片 ,该box属于哪种scale的anchor，网格索引1，网格索引2) + # indices[i].shape=(4, num_i) + b, a, gj, gi = indices[i] # image, anchor, gridy, gridx shape=(num_i) + # tobj.size = (batch_size, 3种scale框, feature_height, feature_width, 1) 全为0 + tobj = torch.zeros_like(pi[..., 0], device=device) # target obj + + n = b.shape[0] # number of GT_targets_filter num + if n: + nt += n # cumulative targets 累加三个检测层中的gt数量 + # 前向传播结果pi.shape(batch_size, 3种scale框, size1, size2, [xywh,score,num_classes,num_angles]) + # b, a, gj, gi shape均=(num_filter)经过筛选的gt信息 pi[该image属于该batch的第几个图片，该box属于哪种scale的anchor，网格索引1，网格索引2] + # 得到ps.size = (经过与gt匹配后筛选的数量N ,[xywh,score,num_classes,num_angles]) + import numpy + # print(pi.shape) + # numpy.savetxt("b.txt", b.numpy(), fmt='%f', delimiter=',') + # print(b.numpy()) + # numpy.savetxt("a.txt", a.numpy(), fmt='%f', delimiter=',') + # print(a.numpy()) + # numpy.savetxt("gj.txt", gj.numpy(), fmt='%f', delimiter=',') + # print(gj.numpy()) + # numpy.savetxt("gi.txt", gi.numpy(), fmt='%f', delimiter=',') + # print(gi.numpy()) + ps = pi[b, a, gj, gi] # prediction subset corresponding to targets 前向传播结果与target信息进行匹配 筛选对应的网格 得到对应网格的前向传播结果 + + # Regression + # pxy.shape(num, 2) + pxy = ps[:, :2].sigmoid() * 2. - 0.5 # 对前向传播结果xy进行回归 （预测的是offset）-> 处理成与当前网格左上角坐标的xy偏移量 + # pxy.shape(num, 2) + pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] # 对前向传播结果wh进行回归 （预测的是当前featuremap尺度上的框wh尺度缩放量）-> 处理成featuremap尺度上框的真实wh + pbox = torch.cat((pxy, pwh), 1).to(device) # predicted box 生成featuremap上的bbox shape(num, 4) + # 计算边框损失，注意这个CIoU=True，计算的是ciou损失 + # 3个tensor组成的list (box[i]) 对每个步长网络生成对应的gt_box tensor + # pbox.T.shape=(4, num) tbox[i].shape=(num, 4) + iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target) shape=(num) + lbox += (1.0 - iou).mean() # iou loss iou为两者的匹配度 因此计算loss时必须让匹配度高的loss贡献更低 因此1-iou后取num个数据的均值 shape(1) + + # Classification 设置如果类别数大于1才计算分类损失 + class_index = 5 + model.nc + if model.nc > 1: # cls loss (only if multiple classes) + # ps.size = (经过与gt匹配后筛选的数量N ,[xywh,score,num_classes,num_angles]) + # t.size = (num ,num_classes) 值全为cn=0（没做标签平滑） + t = torch.full_like(ps[:, 5:class_index], cn, device=device) # targets + # tcls[i] : 对当前步长网络生成对应的class tensor tcls[i].shape=(num, 1) eg：tcls[0] = tensor([73, 73, 73]) + # 在num_classes处对应的类别位置置为cp=1 （没做标签平滑） i为layer index + t[range(n), tcls[i]] = cp + + # 前向传播结果与targets结果开始计算分类损失并累加 + # 筛选后的前向传播结果ps[:, 5:].shape=(num, num_classes) t.shape=(num ,num_classes) + lcls += BCEcls(ps[:, 5:class_index], t) # BCE 分类损失以BCEWithLogitsLoss来计算 + + # Θ类别损失 + if not csl_label_flag: + ttheta = torch.full_like(ps[:, class_index:], cn, device=device) + ttheta[range(n), tangle[i]] = cp + langle += BCEangle(ps[:, class_index:], ttheta) # BCE Θ类别损失以BCEWithLogitsLoss来计算 + else: + ttheta = torch.zeros_like(ps[:, class_index:]) # size(num, 180) + for idx in range(len(ps)): # idx start from 0 to len(ps)-1 + # 3个tensor组成的list (tensor_angle_list[i]) 对每个步长网络生成对应的class tensor tangle[i].shape=(num_i, 1) + theta = tangle[i][idx] # 取出第i个layer中的第idx个目标的角度数值 例如取值θ=90 + # CSL论文中窗口半径为6效果最佳，过小无法学到角度信息，过大则角度预测偏差加大 + csl_label = gaussian_label(theta, 180, u=0, sig=6) # 用长度为1的θ值构建长度为180的csl_label + ttheta[idx] = torch.from_numpy(csl_label) # 将cls_label放入对应的目标中 + langle += BCEangle(ps[:, class_index:], ttheta) + + angle_ = ps[:, class_index:] + angle_value_, angle_index_ = torch.max(angle_, 1, keepdim=True) # size都为 (num, 1) + riou = torch.from_numpy(rbox_iou(pbox, angle_index_, tbox[i], tangle[i].unsqueeze(1))).cuda() + # Objectness 置信度 + # 根据model.gr设置objectness的标签值 + # tobj.size = (batch_size, 3种scale框, size1, size2, 1) 表示该网格预测的是前景（1）还是背景（0） + # 使用标签框与预测框的CIoU值来作为该预测框的conf分支的权重系数 detach不参与网络更新 (1.0 - model.gr)为objectness额外的偏移系数 + tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * riou.detach().clamp(0).type( + tobj.dtype) # iou ratio 与target信息进行匹配 筛选为前景的网格 shape(num) + + # 计算objectness的损失 计算score与labels的损失 + # pi.size = (batch_size, 3种scale框, size1, size2, [xywh,score,num_classes,num_angles]) + # tobj.size = (batch_size, 3种scale框, size1, size2, 1) 其中与gt对应的位置为当前预测框与gt框的?IoU值 ；预测框与gt框的匹配度越高理应预测质量越高 + lobj += BCEobj(pi[..., 4], tobj) * balance[i] # obj loss 最后分别乘上3个尺度检测层的权重并累加 + + # 根据超参数设置的各个部分损失的系数 获取最终损失 + s = 3 / np # output count scaling + lbox *= h['box'] * s + lobj *= h['obj'] * s * (1.4 if np == 4 else 1.) + lcls *= h['cls'] * s + langle *= h['angle'] * s + bs = tobj.shape[0] # batch size + + loss = lbox + lobj + lcls + langle + ''' + loss * bs : 标量 + torch.cat((lbox, lobj, lcls, langle, loss)) : 不参与网络更新的标量 list(边框损失, 置信度损失, 分类损失, Θ分类损失,总损失) + ''' + return loss * bs, torch.cat((lbox, lobj, lcls, langle, loss)).detach() + + +def build_targets(p, targets, model): + """ + Build targets for compute_loss(), input targets(image,class,x,y,w,h)； + Args : + predictions :[small_forward, medium_forward, large_forward] eg:small_forward.size=( batch_size, 3种scale框, size1, size2, no) + targets : torch.Size = (该batch中的目标数量, [该image属于该batch的第几个图片, class, xywh, Θ]) + model : 模型 + + Returns: + tcls : 3个tensor组成的list (tensor_class_list[i]) 对每个步长网络生成对应的class tensor + tcls[i].shape=(num_i, 1) + eg：tcls[0] = tensor([73, 73, 73]) + tbox : 3个tensor组成的list (box[i]) 对每个步长网络生成对应的gt_box信息 xy：当前featuremap尺度上的真实gt_xy与负责预测网格坐标的偏移量; wh：当前featuremap尺度上的真实gt_wh + tbox[i].shape=(num_i, 4) + eg: tbox[0] = tensor([[ 0.19355, 0.27958, 4.38709, 14.92512], + [ 1.19355, 0.27958, 4.38709, 14.92512], + [ 0.19355, 1.27958, 4.38709, 14.92512]]) + indices : 索引列表 也由3个大list组成 每个list代表对每个步长网络生成的索引数据。其中单个list中的索引数据分别有: + (该image属于该batch的第几个图片 ; 该box属于哪种scale的anchor; 网格索引1; 网格索引2) + indices[i].shape=(4, num_i) + eg： indices[0] = (tensor([0, 0, 0]), tensor([1, 1, 1]), tensor([23, 23, 22]), tensor([2, 1, 2])) + anch : anchor列表 也由3个list组成 每个list代表每个步长网络对gt目标采用的anchor大小(对应featuremap尺度上的anchor_wh) + anchor[i].shape=(num_i, 2) + eg：tensor([[2.00000, 3.75000], [2.00000, 3.75000], [2.00000, 3.75000]]) + tangle : 3个tensor组成的list (tensor_angle_list[i]) 对每个步长网络生成对应的angle tensor + tangle[i].shape=(num_i, 1) + eg：tangle[0] = tensor([179, 179, 179]) + """ + # 获取每一个(3个)检测层 + det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module + # anchor数量和GT标签框数量 + na, nt = det.na, targets.shape[0] # number of anchors=3, nums of targets in one batch + tcls, tbox, indices, anch = [], [], [], [] + tangle = [] + gain = torch.ones(8, device=targets.device) # normalized to gridspace gain + # ai.shape = (3, nt) 生成anchor索引 anchor index; ai[0]全等于0. ai[1]全等于1. ai[2]全等于2.用于表示当前gtbox和当前层哪个anchor匹配 + ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + ''' + targets.size(该batch中的GT数量, 7) -> targets.size(3(原来数据的基础上重复三次，按行拼接), 该batch中的GT数量, 7) + targets.size(3(原来数据的基础上重复三次，按行拼接), 该batch中的GT数量, 7) -> targets.size(3(原来数据的基础上重复三次，按行拼接), 该batch中的GT数量, 7 + anchor_index) + targets.shape = (3, num_gt_batch, [该image属于该batch的第几个图片, class, xywh,Θ, 用第几个anchor进行检测]) + 由于每个尺度的feature map各自对应3种scale的anchor，因此将GT标签信息重复三次，方便与每个点的3个anchor单独匹配 + ''' + targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices + + # 设置偏移量 + g = 0.5 # bias 网格中心偏移 + # 附近的四个网格 off.shape = (5, 2) + 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 + + # 对每个检测层进行处理 + for i in range(det.nl): # 3种步长的feature map + # det.anchor(3, 3, 2) anchors: -> 原始anchor(0,:,:)/ 8. , anchor(1,:,:)/ 16. anchor(2,:,:)/ 32. + # anchors (3, 2) 3种scale的anchor wh + anchors = det.anchors[i] # small->medium->large anchor框 + # 得到特征图的坐标系数 + """ + p[i].shape = (b, 3种scale框, h, w, [xywh,score,num_classes,num_angle]), hw分别为特征图的长宽 + gain = [1, 1, h, w, h, w, 1, 1] + """ + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain 把p[i]wh维度的数据赋给gain + num_h, num_w = p[i].shape[2:4] + + # Match targets to anchors + # targets.shape = (3, num_gt_batch, [该image属于该batch的第几个图片, class, xywh,Θ, 用哪个anchor进行检测]) gain = [1, 1, w, h, w, h, 1] + # t.shape = (3 , num_gt_batch, [该image属于该batch的第几个图片, class, xywh_feature,Θ, 用哪个anchor进行检测]) + t = targets * gain # 将labels的归一化的xywh从基于0~1映射到基于特征图的xywh 即变成featuremap尺度 + + if nt: # num_targets 该batch中的目标数量 + # Matches + """ + GT的wh与anchor的wh做匹配，筛选掉比值大于hyp['anchor_t']的(这应该是yolov5的创新点)targets，从而更好的回归(与新的边框回归方式有关) + 若gt_wh/anhor_wh 或 anhor_wh太大/gt_wh 超出hyp['anchor_t']，则说明当前target与所选anchor形状匹配度不高，该物体宽高过于极端，不应强制回归，将该处的labels信息删除，在该层预测中认为是背景 + + 由于yolov3回归wh采用的是out=exp(in)，这很危险，因为out=exp(in)可能会无穷大，就会导致失控的梯度，不稳定，NaN损失并最终完全失去训练； + (当然原yolov3采用的是将targets进行反算来求in与网络输出的结果，就问题不大，但采用iou loss，就需要将网络输出算成out来进行loss求解，所以会面临这个问题)； + 所以作者采用新的wh回归方式: + (wh.sigmoid() * 2) ** 2 * anchors[i], 原来yolov3为anchors[i] * exp(wh) + 将标签框与anchor的倍数控制在0~4之间； + hyp.scratch.yaml中的超参数anchor_t=4，所以也是通过此参数来判定anchors与标签框契合度； + """ + r = t[:, :, 4:6] / anchors[:, None] # wh ratio 获取gt bbox与anchor的wh比值 shape=(3, num_gt_batch, 2) + j = torch.max(r, 1. / r).max(2)[0] < model.hyp['anchor_t'] # compare shape=(3,num_gt_batch) + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + ''' + 从(3 , num_gt_targets_thisbatch,8) 的t中筛选符合条件的anchor_target框 + 即 3 * num_gt_targets_thisbatch个anchor中筛出M个有效GT + 经过第i层检测层筛选过后的t.shape = (M, 8),M为筛选过后的数量 + ''' + # shape=(3 , num_gt_batch_filter, 8) -> (M, [该image属于该batch的第几个图片, class, xywh_feature,Θ, 用哪个anchor进行检测]) + t = t[j] # filter 筛选出与anchor匹配的targets; + + # Offsets + # 得到筛选后的GT的中心点坐标xy-featuremap尺度(相对于左上角的), 其shape为(M, [x_featuremap, y_featuremap]) + gxy = t[:, 2:4] # grid gt xy + # 得到筛选后的GT的中心点相对于右下角的坐标, 其shape为(M, 2) + # gain = [1, 1, w, h, w, h, 1, 1] + gxi = gain[[2, 3]] - gxy # inverse grid gt xy + """ + 把相对于各个网格左上角x 1.)).T # 判断筛选后的GT中心坐标是否相对于各个网格的左上角偏移<0.5 同时 判断 是否不处于最左上角的网格中 （xy两个维度） + l, m = ((gxi % 1. < g) & (gxi > 1.)).T # 判断筛选后的GT中心坐标是否相对于各个网格的右下角偏移<0.5 同时 判断 是否不处于最右下角的网格中 （xy两个维度） + j = torch.stack((torch.ones_like(j), j, k, l, m)) # shape(5, M) 其中元素为True或False + # 由于预设的off为5 先将t在第一个维度重复5次 shape(5, M, 8),现在选出最近的3个(包括 0，0 自己) + t = t.repeat((5, 1, 1))[j] # 得到经过第二次筛选的框(3*M, 8) + + # 添加偏移量 gxy.shape=(M, 2) off.shape = (5, 2) -> shape(5, M, 2) + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] # 选出最近的三个网格 offsets.shape=(3*M, 2) + + else: + t = targets[0] + offsets = 0 + + # Define + # t.size = (3*M, [该image属于该batch的第几个图片, class, xywh_feature,Θ, 用哪个anchor进行检测]) + # b为batch中哪一张图片的索引，c为类别,angle = Θ + b, c = t[:, :2].long().T # image, class + angle = t[:, 6].long() + gxy = t[:, 2:4] # grid xy 不考虑offset时负责预测的网格坐标 xy_featuremap 即feature尺度上的gt真实xy + gwh = t[:, 4:6] # grid wh wh_featuremap + gij = (gxy - offsets).long() # featuremap上的gt真实xy坐标减去偏移量再取整 即计算当前label落在哪个网格坐标上 + gi, gj = gij.T # grid xy indices 将x轴坐标信息压入gi 将y轴坐标索引信息压入gj 负责预测网格具体的整数坐标 比如 23， 2 + gi = torch.clamp(gi, 0, num_w-1) # 确保网格索引不会超过数组的限制 + gj = torch.clamp(gj, 0, num_h-1) + + # Append + a = t[:, 7].long() # anchor indices 表示用第几个anchor进行检测 shape(3*M, 1) + indices.append((b, a, gj, gi)) # image_index, anchor_index, grid indices ; 每个预测层的shape(4, 3*M) + tbox.append(torch.cat((gxy - gij, gwh), 1)) # 每个预测层的box shape(3*M, 4) 其中xy:当前featuremap尺度上的真实gt xy与负责预测网格坐标的偏移量 wh：当前featuremap尺度上的真实gt wh + anch.append(anchors[a]) # anchors 每个预测层的shape(3*M, 2) 当前featuremap尺度上的anchor wh + tcls.append(c) # class 每个预测层的shape(3*M, 1) + tangle.append(angle) # angle 每个预测层的shape(3*M, 1) + ''' + tcls : 3个tensor组成的list (tensor_class_list[i]) 对每个步长网络生成对应的class tensor + tcls[i].shape=(num_i, 1) + eg：tcls[0] = tensor([73, 73, 73]) + tbox : 3个tensor组成的list (box[i]) 对每个步长网络生成对应的gt_box信息 xy：当前featuremap尺度上的真实gt_xy与负责预测网格坐标的偏移量; wh：当前featuremap尺度上的真实gt_wh + tbox[i].shape=(num_i, 4) + eg: tbox[0] = tensor([[ 0.19355, 0.27958, 4.38709, 14.92512], + [ 1.19355, 0.27958, 4.38709, 14.92512], + [ 0.19355, 1.27958, 4.38709, 14.92512]]) + indices : 索引列表 也由3个大list组成 每个list代表对每个步长网络生成的索引数据。其中单个list中的索引数据分别有: + (该image属于该batch的第几个图片 ; 该box属于哪种scale的anchor; 网格索引1; 网格索引2) + indices[i].shape=(4, num_i) + eg： indices[0] = (tensor([0, 0, 0]), tensor([1, 1, 1]), tensor([23, 23, 22]), tensor([2, 1, 2])) + anch : anchor列表 也由3个list组成 每个list代表每个步长网络对gt目标采用的anchor大小(对应featuremap尺度上的anchor_wh) + anchor[i].shape=(num_i, 2) + eg：tensor([[2.00000, 3.75000], [2.00000, 3.75000], [2.00000, 3.75000]]) + tangle : 3个tensor组成的list (tensor_angle_list[i]) 对每个步长网络生成对应的class tensor + tangle[i].shape=(num_i, 1) + eg：tangle[0] = tensor([179, 179, 179]) + ''' + return tcls, tbox, indices, anch, tangle + + +def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, merge=False, classes=None, agnostic=False): + ''' + Performs Non-Maximum Suppression (NMS) on inference results； + @param prediction: size=(batch_size, num_boxes, [xywh,score,num_classes,Θ]) + @param conf_thres: + @param iou_thres: + @param merge: + @param classes: + @param agnostic: + @return: + detections with shape: (batch_size, num_nms_boxes, []) + ''' + + # prediction :(batch_size, num_boxes, [xywh,score,num_classes,Θ]) + nc = prediction[0].shape[1] - 5 # number of classes + class_index = nc + 5 + # xc : (batch_size, num_boxes) 对应位置为1说明该box超过置信度 + 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 单帧图片中的最大目标数量 + 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) + + t = time.time() + # output: (batch_size, ?) + output = [None] * prediction.shape[0] + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x ： (num_boxes,[xywh,score,num_classes,Θ]) + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # 取出数组中索引为True的的值即将置信度符合条件的boxes存入x中 x -> (num_confthres_boxes, [xywh,score,num_classes,Θ]) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:class_index] *= x[:, 4:5] # conf = obj_conf * cls_conf + + angle = x[:, class_index:] # angle.size=(num_confthres_boxes, [num_angles]) + # torch.max(angle,1) 返回每一行中最大值的那个元素，且返回其索引（返回最大元素在这一行的列索引） + # angle_index为预测的θ类别 θ ∈ int[0,179] + angle_value, angle_index = torch.max(angle, 1, keepdim=True) # size都为 (num_confthres_boxes, 1) + # box.size = (num_confthres_boxes, [xywhθ]) θ ∈ [-pi/2, pi/2) length=180 + box = torch.cat((x[:, :4], (angle_index - 90) * np.pi / 180), 1) + + + # Detections matrix nx7 (xywhθ, conf, clsid) θ ∈ [-pi/2, pi/2) + if multi_label: + # nonzero ： 取出每个轴的索引,默认是非0元素的索引（取出括号公式中的为True的元素对应的索引） 将索引号放入i和j中 + # x：(num_confthres_boxes, [xywh,score,num_classes,num_angle]) + # i：num_boxes该维度中的索引号，表示该索引的box其中有class的conf满足要求 length=x中满足条件的所有坐标数量 + # j：num_classes该维度中的索引号，表示某个box中是第j+1类物体的conf满足要求 length=x中满足条件的所有坐标数量 + i, j = (x[:, 5:class_index] > conf_thres).nonzero(as_tuple=False).T + # 按列拼接 list x：(num_confthres_boxes, [xywhθ]+[conf]+[classid]) θ ∈ [-pi/2, pi/2) + x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) # None即新增一个维度 让每个数值单独成为一个维度 + + else: # best class only + conf, j = x[:, 5:class_index].max(1, keepdim=True) + x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] + + # Filter by class 按类别筛选 + if classes: + # list x：(num_confthres_boxes, [xywhθ]+[conf]+[classid]) θ ∈ [-pi/2, pi/2) + x = x[(x[:, 6:7] == torch.tensor(classes, device=x.device)).any(1)] # any（1）函数表示每行满足条件的返回布尔值 + + # 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 + # x : (num_confthres_boxes, [xywhθ]+[conf]+[classid]) θ ∈ [-pi/2, pi/2) + c = x[:, 6:7] * (0 if agnostic else max_wh) # classesid*4096 + boxes, scores = x[:, :5], x[:, 5] # boxes[x, y, w, h, θ] θ is 弧度制[-pi/2, pi/2) + boxes[:, :4] = boxes[:, :4] + c # boxes xywh(offset by class) + + + + if i.shape[0] > max_det: # limit detections 限制单帧图片中检测出的目标数量 + i = i[:max_det] + if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) + try: # 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 + except: # possible CUDA error https://github.com/ultralytics/yolov3/issues/1139 + print(x, i, x.shape, i.shape) + pass + + # output: (batch_size, num_nms_boxes, [x_LT,y_LT,x_RB,y_RB]+conf+class) + output[xi] = x[i] + if (time.time() - t) > time_limit: + break # time limit exceeded + + return output + +def get_rotated_coors(box): + """ + return box coors + @param box: + @return: + """ + assert len(box) > 0 , 'Input valid box!' + cx = box[0]; cy = box[1]; w = box[2]; h = box[3]; a = box[4] + xmin = cx - w*0.5; xmax = cx + w*0.5; ymin = cy - h*0.5; ymax = cy + h*0.5 + t_x0=xmin; t_y0=ymin; t_x1=xmin; t_y1=ymax; t_x2=xmax; t_y2=ymax; t_x3=xmax; t_y3=ymin + R = np.eye(3) + R[:2] = cv2.getRotationMatrix2D(angle=-a*180/math.pi, center=(cx,cy), scale=1) # 获得仿射变化矩阵 + x0 = t_x0*R[0,0] + t_y0*R[0,1] + R[0,2] + y0 = t_x0*R[1,0] + t_y0*R[1,1] + R[1,2] + x1 = t_x1*R[0,0] + t_y1*R[0,1] + R[0,2] + y1 = t_x1*R[1,0] + t_y1*R[1,1] + R[1,2] + x2 = t_x2*R[0,0] + t_y2*R[0,1] + R[0,2] + y2 = t_x2*R[1,0] + t_y2*R[1,1] + R[1,2] + x3 = t_x3*R[0,0] + t_y3*R[0,1] + R[0,2] + y3 = t_x3*R[1,0] + t_y3*R[1,1] + R[1,2] + + if isinstance(x0,torch.Tensor): + r_box=torch.cat([x0.unsqueeze(0),y0.unsqueeze(0), + x1.unsqueeze(0),y1.unsqueeze(0), + x2.unsqueeze(0),y2.unsqueeze(0), + x3.unsqueeze(0),y3.unsqueeze(0)], 0) + else: + r_box = np.array([x0,y0,x1,y1,x2,y2,x3,y3]) + return r_box + +# anchor对齐阶段计算iou +def skewiou(box1, box2,mode='iou',return_coor = False): + a=box1.reshape(4, 2) + b=box2.reshape(4, 2) + # 所有点的最小凸的表示形式，四边形对象，会自动计算四个点，最后顺序为：左上 左下 右下 右上 左上 + poly1 = Polygon(a).convex_hull + poly2 = Polygon(b).convex_hull + if not poly1.is_valid or not poly2.is_valid: + print('formatting errors for boxes!!!! ') + return 0 + if poly1.area == 0 or poly2.area == 0: + return 0 + + inter = Polygon(poly1).intersection(Polygon(poly2)).area + if mode == 'iou': + union = poly1.area + poly2.area - inter + elif mode =='tiou': + union_poly = np.concatenate((a,b)) #合并两个box坐标，变为8*2 + union = MultiPoint(union_poly).convex_hull.area + coors = MultiPoint(union_poly).convex_hull.wkt + elif mode == 'giou': + union_poly = np.concatenate((a,b)) + union = MultiPoint(union_poly).envelope.area + coors = MultiPoint(union_poly).envelope.wkt + elif mode == 'r_giou': + union_poly = np.concatenate((a,b)) + union = MultiPoint(union_poly).minimum_rotated_rectangle.area + coors = MultiPoint(union_poly).minimum_rotated_rectangle.wkt + else: + print('incorrect mode!') + + if union == 0: + return 0 + else: + if return_coor: + return inter/union,coors + else: + return inter/union + + + +def py_cpu_nms_poly(dets, scores,thresh): + """ + 任意四点poly nms.取出nms后的边框的索引 + @param dets: shape(detection_num, [poly]) 原始图像中的检测出的目标数量 + @param thresh: + @return: + keep: 经nms后的目标边框的索引 list + """ + polys = [] + for i in range(len(dets)): + tm_polygon = polyiou.VectorDouble([dets[i][0], dets[i][1], + dets[i][2], dets[i][3], + dets[i][4], dets[i][5], + dets[i][6], dets[i][7]]) + polys.append(tm_polygon) + + # argsort将元素小到大排列 返回索引值 [::-1]即从后向前取元素 + order = scores.argsort()[::-1] # 取出元素的索引值 顺序为从大到小 + keep = [] + while order.size > 0: + ovr = [] + i = order[0] # 取出当前剩余置信度最大的目标边框的索引 + keep.append(i) + for j in range(order.size - 1): # 求出置信度最大poly与其他所有poly的IoU + iou = polyiou.iou_poly(polys[i], polys[order[j + 1]]) + ovr.append(iou) + ovr = np.array(ovr) + inds = np.where(ovr <= thresh)[0] # 找出iou小于阈值的索引 + order = order[inds + 1] + return keep + +def rotate_non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, merge=False, classes=None, agnostic=False, without_iouthres=False): + """ + Performs Rotate-Non-Maximum Suppression (RNMS) on inference results； + @param prediction: size=(batch_size, num, [xywh,score,num_classes,num_angles]) + @param conf_thres: 置信度阈值 + @param iou_thres: IoU阈值 + @param merge: None + @param classes: None + @param agnostic: 进行nms是否将所有类别框一视同仁，默认False + @param without_iouthres : 本次nms不做iou_thres的标志位 默认为False + @return: + output：经nms后的旋转框(batch_size, num_conf_nms, [xywhθ,conf,classid]) θ∈[0,179] + """ + # prediction :(batch_size, num_boxes, [xywh,score,num_classes,num_angles]) + nc = prediction[0].shape[1] - 5 - 180 # number of classes = no - 5 -180 + class_index = nc + 5 + # xc : (batch_size, num_boxes) 对应位置为1说明该box超过置信度 + xc = prediction[..., 4] > conf_thres # candidates + + # Settings + min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height + max_det = 500 # maximum number of detections per image + time_limit = 10.0 # seconds to quit after + redundant = True # require redundant detections 要求冗余检测 + multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img) + + t = time.time() + # output: (batch_size, ?) + output = [None] * prediction.shape[0] + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x ： (num_boxes, [xywh, score, num_classes, num_angles]) + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # 取出数组中索引为True的的值即将置信度符合条件的boxes存入x中 x -> (num_confthres_boxes, no) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:class_index] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box (center x, center y, width, height) to (x1, y1, x2, y2) + angle = x[:, class_index:] # angle.size=(num_confthres_boxes, [num_angles]) + # torch.max(angle,1) 返回每一行中最大值的那个元素，且返回其索引（返回最大元素在这一行的列索引） + angle_value, angle_index = torch.max(angle, 1, keepdim=True) # size都为 (num_confthres_boxes, 1) + # box.size = (num_confthres_boxes, [xywhθ]) θ∈[0,179] + box = torch.cat((x[:, :4], angle_index), 1) + if multi_label: + # nonzero ： 取出每个轴的索引,默认是非0元素的索引（取出括号公式中的为True的元素对应的索引） 将索引号放入i和j中 + # i：num_boxes该维度中的索引号，表示该索引的box其中有class的conf满足要求 length=x中满足条件的所有坐标数量 + # j：num_classes该维度中的索引号，表示某个box中是第j+1类物体的conf满足要求 length=x中满足条件的所有坐标数量 + i, j = (x[:, 5:class_index] > conf_thres).nonzero(as_tuple=False).T + # 按列拼接 list x：(num_confthres_boxes, [xywhθ]+[conf]+[classid]) θ∈[0,179] + x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) + + else: # best class only + conf, j = x[:, 5:class_index].max(1, keepdim=True) + x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] + + if without_iouthres: # 不做nms_iou + output[xi] = x + continue + + # Filter by class 按类别筛选 + if classes: + # list x：(num_confthres_boxes, [xywhθ,conf,classid]) + x = x[(x[:, 6:7] == torch.tensor(classes, device=x.device)).any(1)] # any（1）函数表示每行满足条件的返回布尔值 + + # 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 + # x：(num_confthres_boxes, [xywhθ,conf,classid]) θ∈[0,179] + c = x[:, 6:7] * (0 if agnostic else max_wh) # classes + # boxes:(num_confthres_boxes, [xy]) scores:(num_confthres_boxes, 1) + # agnostic用于 不同类别的框仅跟自己类别的目标进行nms (offset by class) 类别id越大,offset越大 + boxes_xy, box_whthetas, scores = x[:, :2] + c, x[:, 2:5], x[:, 5] + rects = [] + for i, box_xy in enumerate(boxes_xy): + rect = longsideformat2poly(box_xy[0], box_xy[1], box_whthetas[i][0], box_whthetas[i][1], box_whthetas[i][2]) + rects.append(rect) + i = np.array(py_cpu_nms_poly(np.array(rects), np.array(scores.cpu()), iou_thres)) + #i = nms(boxes, scores) # i为数组，里面存放着boxes中经nms后的索引 + + 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) + try: # 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 + except: # possible CUDA error https://github.com/ultralytics/yolov3/issues/1139 + print(x, i, x.shape, i.shape) + pass + + output[xi] = x[i] # 根据nms索引提取x中的框 x.size=(num_conf_nms, [xywhθ,conf,classid]) θ∈[0,179] + + 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 coco_class_count(path='../coco/labels/train2014/'): + # Histogram of occurrences per class + nc = 80 # number classes + x = np.zeros(nc, dtype='int32') + files = sorted(glob.glob('%s/*.*' % path)) + for i, file in enumerate(files): + labels = np.loadtxt(file, dtype=np.float32).reshape(-1, 5) + x += np.bincount(labels[:, 0].astype('int32'), minlength=nc) + print(i, len(files)) + + +def coco_only_people(path='../coco/labels/train2017/'): # from utils.general import *; coco_only_people() + # Find images with only people + files = sorted(glob.glob('%s/*.*' % path)) + for i, file in enumerate(files): + labels = np.loadtxt(file, dtype=np.float32).reshape(-1, 5) + if all(labels[:, 0] == 0): + print(labels.shape[0], file) + + +def crop_images_random(path='../images/', scale=0.50): # from utils.general import *; crop_images_random() + # crops images into random squares up to scale fraction + # WARNING: overwrites images! + for file in tqdm(sorted(glob.glob('%s/*.*' % path))): + img = cv2.imread(file) # BGR + if img is not None: + h, w = img.shape[:2] + + # create random mask + a = 30 # minimum size (pixels) + mask_h = random.randint(a, int(max(a, h * scale))) # mask height + mask_w = mask_h # mask width + + # box + xmin = max(0, random.randint(0, w) - mask_w // 2) + ymin = max(0, random.randint(0, h) - mask_h // 2) + xmax = min(w, xmin + mask_w) + ymax = min(h, ymin + mask_h) + + # apply random color mask + cv2.imwrite(file, img[ymin:ymax, xmin:xmax]) + + +def coco_single_class_labels(path='../coco/labels/train2014/', label_class=43): + # Makes single-class coco datasets. from utils.general import *; coco_single_class_labels() + if os.path.exists('new/'): + shutil.rmtree('new/') # delete output folder + os.makedirs('new/') # make new output folder + os.makedirs('new/labels/') + os.makedirs('new/images/') + for file in tqdm(sorted(glob.glob('%s/*.*' % path))): + with open(file, 'r') as f: + labels = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32) + i = labels[:, 0] == label_class + if any(i): + img_file = file.replace('labels', 'images').replace('txt', 'jpg') + labels[:, 0] = 0 # reset class to 0 + with open('new/images.txt', 'a') as f: # add image to dataset list + f.write(img_file + '\n') + with open('new/labels/' + Path(file).name, 'a') as f: # write label + for l in labels[i]: + f.write('%g %.6f %.6f %.6f %.6f\n' % tuple(l)) + shutil.copyfile(src=img_file, dst='new/images/' + Path(file).name.replace('txt', 'jpg')) # copy images + + +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 + + Return: + k: kmeans evolved anchors + + Usage: + from utils.general 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 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) # unflitered + 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 = 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 = 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) + + +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 fitness(x): + # Returns fitness (for use with results.txt or evolve.txt) + 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 output_to_target(output, width, height): + ''' + Convert model output to target format [batch_id, class_id, x, y, w, h, θ, conf] + @param output: (batch_size, num_conf_nms, [xywhθ,conf,classid]) θ∈[0,179] 真实xywh + @param width: width + @param height: height + @return: + targets: (该batch中的目标数量, [该image属于该batch的第几个图片, class, xywh, θ , conf] 归一化的xywh + ''' + if isinstance(output, torch.Tensor): + output = output.cpu().numpy() + + targets = [] + for i, o in enumerate(output): + if o is not None: # o.size = (num_conf_nms, [xywhθ,conf,classid]) θ∈[0,179] + for pred in o: # pred.size = [xywhθ,conf,classid] + box = pred[:4] + w = box[2] / width + h = box[3] / height + x = box[0] / width + y = box[1] / height + conf = pred[5] + cls = int(pred[6]) + + targets.append([i, cls, x, y, w, h, pred[4], conf]) + + return np.array(targets) + + +def increment_dir(dir, comment=''): + # Increments a directory runs/exp1 --> runs/exp2_comment + n = 0 # number + dir = str(Path(dir)) # os-agnostic + d = sorted(glob.glob(dir + '*')) # directories + if len(d): + n = max([int(x[len(dir):x.rfind('_') if '_' in Path(x).name else None]) for x in d]) + 1 # increment + return dir + str(n) + ('_' + comment if comment else '') + + +# Plotting functions --------------------------------------------------------------------------------------------------- +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 + b, a = butter(order, normal_cutoff, btype='low', analog=False) + return b, a + + 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 + @param x: [tensor(x1),tensor(y1),tensor(x2),tensor(y2)] + @param img: 原始图片 shape=(size1,size2,3) + @param color: size(3) eg:[25, 184, 176] + @param label: 字符串 + @param line_thickness: 框的厚度 + ''' + 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 longsideformat2poly(x_c, y_c, longside, shortside, theta_longside): + ''' + trans longside format(x_c, y_c, longside, shortside, θ) θ ∈ [0-179] to poly + @param x_c: center_x + @param y_c: center_y + @param longside: 最长边 + @param shortside: 最短边 + @param theta_longside: 最长边和x轴逆时针旋转的夹角，逆时针方向角度为负 [0, 180) + @return: poly shape(8) + ''' + # Θ:flaot[0-179] -> (-180,0) + rect = longsideformat2cvminAreaRect(x_c, y_c, longside, shortside, (theta_longside - 179.9)) + # poly = [(x1,y1),(x2,y2),(x3,y3),(x4,y4)] + poly = np.double(cv2.boxPoints(rect)) # 返回rect对应的四个点的值 normalized + poly.shape = 8 + return poly + +def cvminAreaRect2longsideformat(x_c, y_c, width, height, theta): + ''' + trans minAreaRect(x_c, y_c, width, height, θ) to longside format(x_c, y_c, longside, shortside, θ) + 两者区别为: + 当opencv表示法中width为最长边时（包括正方形的情况），则两种表示方法一致 + 当opencv表示法中width不为最长边 ，则最长边表示法的角度要在opencv的Θ基础上-90度 + @param x_c: center_x + @param y_c: center_y + @param width: x轴逆时针旋转碰到的第一条边 + @param height: 与width不同的边 + @param theta: x轴逆时针旋转与width的夹角，由于原点位于图像的左上角，逆时针旋转角度为负 [-90, 0) + @return: + x_c: center_x + y_c: center_y + longside: 最长边 + shortside: 最短边 + theta_longside: 最长边和x轴逆时针旋转的夹角，逆时针方向角度为负 [-180, 0) + ''' + ''' + 意外情况:(此时要将它们恢复符合规则的opencv形式：wh交换，Θ置为-90) + 竖直box：box_width < box_height θ=0 + 水平box：box_width > box_height θ=0 + ''' + if theta == 0: + theta = -90 + buffer_width = width + width = height + height = buffer_width + + if theta > 0: + if theta != 90: # Θ=90说明wh中有为0的元素，即gt信息不完整，无需提示异常，直接删除 + print('θ计算出现异常，当前数据为：%.16f, %.16f, %.16f, %.16f, %.1f;超出opencv表示法的范围：[-90,0)' % (x_c, y_c, width, height, theta)) + return False + + if theta < -90: + print('θ计算出现异常，当前数据为：%.16f, %.16f, %.16f, %.16f, %.1f;超出opencv表示法的范围：[-90,0)' % (x_c, y_c, width, height, theta)) + return False + + if width != max(width, height): # 若width不是最长边 + longside = height + shortside = width + theta_longside = theta - 90 + else: # 若width是最长边(包括正方形的情况) + longside = width + shortside = height + theta_longside = theta + + if longside < shortside: + print('旋转框转换表示形式后出现问题：最长边小于短边;[%.16f, %.16f, %.16f, %.16f, %.1f]' % (x_c, y_c, longside, shortside, theta_longside)) + return False + if (theta_longside < -180 or theta_longside >= 0): + print('旋转框转换表示形式时出现问题:θ超出长边表示法的范围：[-180,0);[%.16f, %.16f, %.16f, %.16f, %.1f]' % (x_c, y_c, longside, shortside, theta_longside)) + return False + + return x_c, y_c, longside, shortside, theta_longside + +def longsideformat2cvminAreaRect(x_c, y_c, longside, shortside, theta_longside): + ''' + trans longside format(x_c, y_c, longside, shortside, θ) to minAreaRect(x_c, y_c, width, height, θ) + 两者区别为: + 当opencv表示法中width为最长边时（包括正方形的情况），则两种表示方法一致 + 当opencv表示法中width不为最长边 ，则最长边表示法的角度要在opencv的Θ基础上-90度 + @param x_c: center_x + @param y_c: center_y + @param longside: 最长边 + @param shortside: 最短边 + @param theta_longside: 最长边和x轴逆时针旋转的夹角，逆时针方向角度为负 [-180, 0) + @return: ((x_c, y_c),(width, height),Θ) + x_c: center_x + y_c: center_y + width: x轴逆时针旋转碰到的第一条边最长边 + height: 与width不同的边 + theta: x轴逆时针旋转与width的夹角，由于原点位于图像的左上角，逆时针旋转角度为负 [-90, 0) + ''' + if ((theta_longside >= -180) and (theta_longside < -90)): # width is not the longest side + width = shortside + height = longside + theta = theta_longside + 90 + else: + width = longside + height =shortside + theta = theta_longside + + if (theta < -90) or (theta >= 0): + print('当前θ=%.1f，超出opencv的θ定义范围[-90, 0)' % theta) + + return ((x_c, y_c), (width, height), theta) + +def plot_one_rotated_box(rbox, img, color=None, label=None, line_thickness=None, pi_format=True): + ''' + Plots one rotated bounding box on image img + @param rbox:[tensor(x),tensor(y),tensor(l),tensor(s),tensor(θ)] + @param img: 原始图片 shape=(size1,size2,3) + @param color: size(3) eg:[25, 184, 176] + @param label: 字符串 + @param line_thickness: 框的厚度 + @param pi_format: θ是否为pi且 θ ∈ [-pi/2,pi/2) False说明 θ∈[0,179] + ''' + if isinstance(rbox, torch.Tensor): + rbox = rbox.cpu().float().numpy() + + 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)] + #rbox = np.array(x) + if pi_format: # θ∈[-pi/2,pi/2) + rbox[-1] = (rbox[-1] * 180 / np.pi) + 90 # θ∈[0,179] + + # rect=[(x_c,y_c),(w,h),Θ] Θ:flaot[0-179] -> (-180,0) + rect = longsideformat2cvminAreaRect(rbox[0], rbox[1], rbox[2], rbox[3], (rbox[4] - 179.9)) + # poly = [(x1,y1),(x2,y2),(x3,y3),(x4,y4)] + poly = np.float32(cv2.boxPoints(rect)) # 返回rect对应的四个点的值 + poly = np.int0(poly) + # 画出来 + cv2.drawContours(image=img, contours=[poly], contourIdx=-1, color=color, thickness=2*tl) + c1 = (int(rbox[0]), int(rbox[1])) + if label: + tf = max(tl - 1, 1) # font thickness + t_size = cv2.getTextSize(label, 0, fontScale=tl / 4, 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 / 4, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) + + + + +def plot_wh_methods(): # from utils.general 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 plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=4): + """ + 将batch中的图片绘制在一张图中 + @param images: torch.Size([batch_size, 3, weights, heights]) + @param targets: torch.Size = (该batch中的目标数量, [该image属于该batch的第几个图片, class, xywh, θ , conf(maybe)] + @param paths: List['img1_path','img2_path',......,'img-1_path'] len(paths)=batch_size + @param fname: save_filename + @param max_subplots: 一张图中最多绘制的图片数量（最多在一张图中绘制batch_size张图片 or max_subplots张图片） + + @return: mosaic 将该batch的图片绘制在一张图中（绘制的图片数量由max_subplots确定） + """ + tl = 3 # line thickness + tf = max(tl - 1, 1) # font thickness + + if isinstance(images, torch.Tensor): + images = images.cpu().float().numpy() + + if isinstance(targets, torch.Tensor): + targets = targets.cpu().numpy() + + # un-normalise 将归一化的图像还原 + if np.max(images[0]) <= 1: + images *= 255 + + bs, _, h, w = images.shape # batch size, _, height, width + bs = min(bs, max_subplots) # limit plot images 保存的图中最多一次性绘制batch_size张图（不超过max_subplots=16） + ns = np.ceil(bs ** 0.5) # number of subplots (square) 比如batch_size为4则 subplots为2 （2*2=4） + + # 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) + + # Empty array for output + mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) + + # Fix class - colour map + prop_cycle = plt.rcParams['axes.prop_cycle'] + # https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb + hex2rgb = lambda h: tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) + color_lut = [hex2rgb(h) for h in prop_cycle.by_key()['color']] + + # torch.Size([batch_size, 3, weights, heights]) + 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 + # torch.Size = (该batch中的目标数量, [该image属于该batch的第几个图片, class, xywh, θ , conf(maybe)] + if len(targets) > 0: + image_targets = targets[targets[:, 0] == i] + boxes = xywh2xyxy(image_targets[:, 2:6]).T + classes = image_targets[:, 1].astype('int') + gt = image_targets.shape[1] == 7 # ground truth if no conf column + theta = image_targets[:, 6] # numpy.size=(num) -> (num, 1) + theta = theta[:, None] + conf = None if gt else image_targets[:, 7] # check for confidence presence (gt vs pred) + + boxes[[0, 2]] *= w + boxes[[0, 2]] += block_x + boxes[[1, 3]] *= h + boxes[[1, 3]] += block_y + + boxes = xyxy2xywh(boxes.T) # numpy.size=(num, [xywh]) + rboxes = np.hstack((boxes, theta)) # numpy.size=(num, [xywhθ]) + for j, rbox in enumerate(rboxes): + cls = int(classes[j]) + color = color_lut[cls % len(color_lut)] + cls = names[cls] if names else cls + if gt or conf[j] > 0.3: # 0.3 conf thresh + label = '%s' % cls if gt else '%s %.1f' % (cls, conf[j]) + #plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl) + plot_one_rotated_box(rbox, mosaic, label=label, color=color, line_thickness=tl, + pi_format=False) + + # Draw image filename labels + if paths is not None: + label = os.path.basename(paths[i])[:40] # trim to 40 char + t_size = cv2.getTextSize(label, 0, fontScale=tl / 4, thickness=tf)[0] + cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 4, [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 is not None: + mosaic = cv2.resize(mosaic, (int(ns * w * 0.5), int(ns * h * 0.5)), interpolation=cv2.INTER_AREA) + cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB)) + + 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.general 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.general 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(f='study.txt', x=None): # from utils.general 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 ['study/study_coco_yolov5%s.txt' % x for x in ['s', 'm', 'l', 'x']]: + 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=Path(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(28, 50) + ax2.set_yticks(np.arange(30, 55, 5)) + ax2.set_xlabel('GPU Speed (ms/img)') + ax2.set_ylabel('COCO AP val') + ax2.legend(loc='lower right') + plt.savefig('study_mAP_latency.png', dpi=300) + plt.savefig(f.replace('.txt', '.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 + + fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) + ax = ax.ravel() + ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) + ax[0].set_xlabel('classes') + ax[1].scatter(b[0], b[1], c=hist2d(b[0], b[1], 90), cmap='jet') + ax[1].set_xlabel('x') + ax[1].set_ylabel('y') + ax[2].scatter(b[2], b[3], c=hist2d(b[2], b[3], 90), cmap='jet') + ax[2].set_xlabel('width') + ax[2].set_ylabel('height') + plt.savefig(Path(save_dir) / 'labels.png', dpi=200) + plt.close() + + # 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 + + +def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.general 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.general 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=''): + # from utils.general import *; plot_results() + # Plot training 'results*.txt' as seen in https://github.com/ultralytics/yolov5#reproduce-our-training + 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: + # os.system('rm -rf storage.googleapis.com') + # 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 = glob.glob(str(Path(save_dir) / 'results*.txt')) + glob.glob('../../Downloads/results*.txt') + 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 # dont show zero loss values + # y /= y[0] # normalize + label = labels[fi] if len(labels) else Path(f).stem + ax[i].plot(x, y, marker='.', label=label, linewidth=1, markersize=6) + 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) diff --git a/utils/google_app_engine/Dockerfile b/utils/google_app_engine/Dockerfile new file mode 100644 index 00000000..0155618f --- /dev/null +++ b/utils/google_app_engine/Dockerfile @@ -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 diff --git a/utils/google_app_engine/additional_requirements.txt b/utils/google_app_engine/additional_requirements.txt new file mode 100644 index 00000000..5fcc3052 --- /dev/null +++ b/utils/google_app_engine/additional_requirements.txt @@ -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 diff --git a/utils/google_app_engine/app.yaml b/utils/google_app_engine/app.yaml new file mode 100644 index 00000000..ac29d104 --- /dev/null +++ b/utils/google_app_engine/app.yaml @@ -0,0 +1,14 @@ +runtime: custom +env: flex + +service: yolov5app + +liveness_check: + initial_delay_sec: 600 + +manual_scaling: + instances: 1 +resources: + cpu: 1 + memory_gb: 4 + disk_size_gb: 20 \ No newline at end of file diff --git a/utils/google_utils.py b/utils/google_utils.py new file mode 100644 index 00000000..649bbe58 --- /dev/null +++ b/utils/google_utils.py @@ -0,0 +1,125 @@ +# This file contains google utils: https://cloud.google.com/storage/docs/reference/libraries +# pip install --upgrade google-cloud-storage +# from google.cloud import storage + +import os +import platform +import subprocess +import time +from pathlib import Path + +import torch + + +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 attempt_download(weights): + ''' + # Attempt to download pretrained weights if not found locally + # 如果在本地找不到，请尝试下载经过预训练的权重 + ''' + weights = weights.strip().replace("'", '') + file = Path(weights).name + + msg = weights + ' missing, try downloading from https://github.com/ultralytics/yolov5/releases/' + models = ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt'] # available models + + 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/yolov5/releases/download/v3.0/' + 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) + url = 'https://storage.googleapis.com/ultralytics/yolov5/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='') + os.remove(name) if os.path.exists(name) else None # remove existing + os.remove('cookie') if os.path.exists('cookie') else None + + # Attempt file download + 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 "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 "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 + if r != 0: + os.remove(name) if os.path.exists(name) else None # remove partial + print('Download error ') # raise Exception('Download error') + return r + + # Unzip if archive + if name.endswith('.zip'): + print('unzipping... ', end='') + os.system('unzip -q %s' % name) # unzip + os.remove(name) # remove zip to free space + + print('Done (%.1fs)' % (time.time() - t)) + return r + + +def get_token(cookie="./cookie"): + with open(cookie) as f: + for line in f: + if "download" in line: + return line.split()[-1] + return "" + +# 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)) diff --git a/utils/poly_nms_gpu/Makefile b/utils/poly_nms_gpu/Makefile new file mode 100644 index 00000000..a4823985 --- /dev/null +++ b/utils/poly_nms_gpu/Makefile @@ -0,0 +1,3 @@ +all: + python setup.py build_ext --inplace + rm -rf build diff --git a/utils/poly_nms_gpu/__init__.py b/utils/poly_nms_gpu/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/utils/poly_nms_gpu/nms_wrapper.py b/utils/poly_nms_gpu/nms_wrapper.py new file mode 100644 index 00000000..768b8a39 --- /dev/null +++ b/utils/poly_nms_gpu/nms_wrapper.py @@ -0,0 +1,17 @@ +# -------------------------------------------------------- +# Fast R-CNN +# Copyright (c) 2015 Microsoft +# Licensed under The MIT License [see LICENSE for details] +# Written by Ross Girshick +# -------------------------------------------------------- + +# from nms.gpu_nms import gpu_nms +# from nms.cpu_nms import cpu_nms +from .poly_nms import poly_gpu_nms +def poly_nms_gpu(dets, thresh, force_cpu=False): + """Dispatch to either CPU or GPU NMS implementations.""" + + if dets.shape[0] == 0: + return [] + return poly_gpu_nms(dets, thresh, device_id=0) + diff --git a/utils/poly_nms_gpu/poly_nms.hpp b/utils/poly_nms_gpu/poly_nms.hpp new file mode 100644 index 00000000..61f2df75 --- /dev/null +++ b/utils/poly_nms_gpu/poly_nms.hpp @@ -0,0 +1,12 @@ +// +// Created by dingjian on 18-5-24. +// + +#ifndef DOTA_DEVKIT_POLY_NMS_HPP +#define DOTA_DEVKIT_POLY_NMS_HPP + + +void _poly_nms(int* keep_out, int* num_out, const float* polys_host, int polys_num, + int polys_dim, float nms_overlap_thresh, int device_id); + +#endif //DOTA_DEVKIT_POLY_NMS_HPP diff --git a/utils/poly_nms_gpu/poly_nms.pyx b/utils/poly_nms_gpu/poly_nms.pyx new file mode 100644 index 00000000..100ec331 --- /dev/null +++ b/utils/poly_nms_gpu/poly_nms.pyx @@ -0,0 +1,24 @@ +import numpy as np +cimport numpy as np + +assert sizeof(int) == sizeof(np.int32_t) + +cdef extern from "poly_nms.hpp": + void _poly_nms(np.int32_t*, int*, np.float32_t*, int, int, float, int) + +def poly_gpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh, + np.int32_t device_id=0): + cdef int boxes_num = dets.shape[0] + cdef int boxes_dim = dets.shape[1] + cdef int num_out + cdef np.ndarray[np.int32_t, ndim=1] \ + keep = np.zeros(boxes_num, dtype=np.int32) + cdef np.ndarray[np.float32_t, ndim=1] \ + scores = dets[:, 8] + cdef np.ndarray[np.int_t, ndim=1] \ + order = scores.argsort()[::-1] + cdef np.ndarray[np.float32_t, ndim=2] \ + sorted_dets = dets[order, :] + _poly_nms(&keep[0], &num_out, &sorted_dets[0, 0], boxes_num, boxes_dim, thresh, device_id) + keep = keep[:num_out] + return list(order[keep]) diff --git a/utils/poly_nms_gpu/poly_nms_kernel.cu b/utils/poly_nms_gpu/poly_nms_kernel.cu new file mode 100644 index 00000000..8f6a2cbe --- /dev/null +++ b/utils/poly_nms_gpu/poly_nms_kernel.cu @@ -0,0 +1,346 @@ + +#include "poly_nms.hpp" +#include +#include +#include +#include +#include + +using namespace std; + +//##define CUDA_CHECK(condition)\ +// +// do { +// cudaError_t error = condition; +// if (error != cudaSuccess) { +// +// } +// } + +#define CUDA_CHECK(condition) \ + /* Code block avoids redefinition of cudaError_t error */ \ + do { \ + cudaError_t error = condition; \ + if (error != cudaSuccess) { \ + std::cout << cudaGetErrorString(error) << std::endl; \ + } \ + } while (0) + +#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0)) +int const threadsPerBlock = sizeof(unsigned long long) * 8; + + +#define maxn 51 +const double eps=1E-8; + +__device__ inline int sig(float d){ + return(d>eps)-(d<-eps); +} +// struct Point{ +// double x,y; Point(){} +// Point(double x,double y):x(x),y(y){} +// bool operator==(const Point&p)const{ +// return sig(x-p.x)==0&&sig(y-p.y)==0; +// } +// }; + +__device__ inline int point_eq(const float2 a, const float2 b) { + return sig(a.x - b.x) == 0 && sig(a.y - b.y)==0; +} + +__device__ inline void point_swap(float2 *a, float2 *b) { + float2 temp = *a; + *a = *b; + *b = temp; +} + +__device__ inline void point_reverse(float2 *first, float2* last) +{ + while ((first!=last)&&(first!=--last)) { + point_swap (first,last); + ++first; + } +} +// void point_reverse(Point* first, Point* last) +// { +// while ((first!=last)&&(first!=--last)) { +// point_swap (first,last); +// ++first; +// } +// } + + +__device__ inline float cross(float2 o,float2 a,float2 b){ //叉积 + return(a.x-o.x)*(b.y-o.y)-(b.x-o.x)*(a.y-o.y); +} +__device__ inline float area(float2* ps,int n){ + ps[n]=ps[0]; + float res=0; + for(int i=0;i0) pp[m++]=p[i]; +// if(sig(cross(a,b,p[i]))!=sig(cross(a,b,p[i+1]))) +// lineCross(a,b,p[i],p[i+1],pp[m++]); +// } +// n=0; +// for(int i=0;i1&&p[n-1]==p[0])n--; +// while(n>1&&point_eq(p[n-1], p[0]))n--; +// } + +__device__ inline void polygon_cut(float2*p,int&n,float2 a,float2 b, float2* pp){ + + int m=0;p[n]=p[0]; + for(int i=0;i0) pp[m++]=p[i]; + if(sig(cross(a,b,p[i]))!=sig(cross(a,b,p[i+1]))) + lineCross(a,b,p[i],p[i+1],pp[m++]); + } + n=0; + for(int i=0;i1&&p[n-1]==p[0])n--; + while(n>1&&point_eq(p[n-1], p[0]))n--; +} + +//---------------华丽的分隔线-----------------// +//返回三角形oab和三角形ocd的有向交面积,o是原点// +__device__ inline float intersectArea(float2 a,float2 b,float2 c,float2 d){ + float2 o = make_float2(0,0); + int s1=sig(cross(o,a,b)); + int s2=sig(cross(o,c,d)); + if(s1==0||s2==0)return 0.0;//退化，面积为0 + // if(s1==-1) swap(a,b); + // if(s2==-1) swap(c,d); + if (s1 == -1) point_swap(&a, &b); + if (s2 == -1) point_swap(&c, &d); + float2 p[10]={o,a,b}; + int n=3; + float2 pp[maxn]; + polygon_cut(p,n,o,c,pp); + polygon_cut(p,n,c,d,pp); + polygon_cut(p,n,d,o,pp); + float res=fabs(area(p,n)); + if(s1*s2==-1) res=-res;return res; +} +//求两多边形的交面积 +__device__ inline float intersectArea(float2*ps1,int n1,float2*ps2,int n2){ + if(area(ps1,n1)<0) point_reverse(ps1,ps1+n1); + if(area(ps2,n2)<0) point_reverse(ps2,ps2+n2); + ps1[n1]=ps1[0]; + ps2[n2]=ps2[0]; + float res=0; + for(int i=0;i p, vector q) { +// Point ps1[maxn],ps2[maxn]; +// int n1 = 4; +// int n2 = 4; +// for (int i = 0; i < 4; i++) { +// ps1[i].x = p[i * 2]; +// ps1[i].y = p[i * 2 + 1]; +// +// ps2[i].x = q[i * 2]; +// ps2[i].y = q[i * 2 + 1]; +// } +// double inter_area = intersectArea(ps1, n1, ps2, n2); +// double union_area = fabs(area(ps1, n1)) + fabs(area(ps2, n2)) - inter_area; +// double iou = inter_area / union_area; +// +//// cout << "inter_area:" << inter_area << endl; +//// cout << "union_area:" << union_area << endl; +//// cout << "iou:" << iou << endl; +// +// return iou; +//} + +__device__ inline float devPolyIoU(float const * const p, float const * const q) { + float2 ps1[maxn], ps2[maxn]; + int n1 = 4; + int n2 = 4; + for (int i = 0; i < 4; i++) { + ps1[i].x = p[i * 2]; + ps1[i].y = p[i * 2 + 1]; + + ps2[i].x = q[i * 2]; + ps2[i].y = q[i * 2 + 1]; + } + float inter_area = intersectArea(ps1, n1, ps2, n2); + float union_area = fabs(area(ps1, n1)) + fabs(area(ps2, n2)) - inter_area; + float iou = 0; + if (union_area == 0) { + iou = (inter_area + 1) / (union_area + 1); + } else { + iou = inter_area / union_area; + } + return iou; +} + +__global__ void poly_nms_kernel(const int n_polys, const float nms_overlap_thresh, + const float *dev_polys, unsigned long long *dev_mask) { + const int row_start = blockIdx.y; + const int col_start = blockIdx.x; + + const int row_size = + min(n_polys - row_start * threadsPerBlock, threadsPerBlock); + const int cols_size = + min(n_polys - col_start * threadsPerBlock, threadsPerBlock); + + __shared__ float block_polys[threadsPerBlock * 9]; + if (threadIdx.x < cols_size) { + block_polys[threadIdx.x * 9 + 0] = + dev_polys[(threadsPerBlock * col_start + threadIdx.x) * 9 + 0]; + block_polys[threadIdx.x * 9 + 1] = + dev_polys[(threadsPerBlock * col_start + threadIdx.x) * 9 + 1]; + block_polys[threadIdx.x * 9 + 2] = + dev_polys[(threadsPerBlock * col_start + threadIdx.x) * 9 + 2]; + block_polys[threadIdx.x * 9 + 3] = + dev_polys[(threadsPerBlock * col_start + threadIdx.x) * 9 + 3]; + block_polys[threadIdx.x * 9 + 4] = + dev_polys[(threadsPerBlock * col_start + threadIdx.x) * 9 + 4]; + block_polys[threadIdx.x * 9 + 5] = + dev_polys[(threadsPerBlock * col_start + threadIdx.x) * 9 + 5]; + block_polys[threadIdx.x * 9 + 6] = + dev_polys[(threadsPerBlock * col_start + threadIdx.x) * 9 + 6]; + block_polys[threadIdx.x * 9 + 7] = + dev_polys[(threadsPerBlock * col_start + threadIdx.x) * 9 + 7]; + block_polys[threadIdx.x * 9 + 8] = + dev_polys[(threadsPerBlock * col_start + threadIdx.x) * 9 + 8]; + } + __syncthreads(); + + if (threadIdx.x < row_size) { + const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x; + const float *cur_box = dev_polys + cur_box_idx * 9; + int i = 0; + unsigned long long t = 0; + int start = 0; + if (row_start == col_start) { + start = threadIdx.x + 1; + } + for (i = start; i < cols_size; i++) { + if (devPolyIoU(cur_box, block_polys + i * 9) > nms_overlap_thresh) { + t |= 1ULL << i; + } + } + const int col_blocks = DIVUP(n_polys, threadsPerBlock); + dev_mask[cur_box_idx * col_blocks + col_start] = t; + } +} + +void _set_device(int device_id) { + int current_device; + CUDA_CHECK(cudaGetDevice(¤t_device)); + if (current_device == device_id) { + return; + } + // The call to cudaSetDevice must come before any calls to Get, which + // may perform initailization using the GPU. + CUDA_CHECK(cudaSetDevice(device_id)); +} + +void _poly_nms(int* keep_out, int* num_out, const float* polys_host, int polys_num, + int polys_dim, float nms_overlap_thresh, int device_id) { + float* polys_dev = NULL; + unsigned long long* mask_dev = NULL; + const int col_blocks = DIVUP(polys_num, threadsPerBlock); + + CUDA_CHECK(cudaMalloc(&polys_dev, + polys_num * polys_dim * sizeof(float))); + CUDA_CHECK(cudaMemcpy(polys_dev, + polys_host, + polys_num * polys_dim * sizeof(float), + cudaMemcpyHostToDevice)); + + CUDA_CHECK(cudaMalloc(&mask_dev, + polys_num * col_blocks * sizeof(unsigned long long))); + + dim3 blocks(DIVUP(polys_num, threadsPerBlock), + DIVUP(polys_num, threadsPerBlock)); + dim3 threads(threadsPerBlock); +// __global__ void poly_nms_kernel(const int n_polys, const float nms_overlap_thresh, +// const float *dev_polys, unsigned long long *dev_mask) + poly_nms_kernel<<>>(polys_num, + nms_overlap_thresh, + polys_dev, + mask_dev); + + std::vector mask_host(polys_num * col_blocks); + CUDA_CHECK(cudaMemcpy(&mask_host[0], + mask_dev, + sizeof(unsigned long long) * polys_num * col_blocks, + cudaMemcpyDeviceToHost)); + + std::vector remv(col_blocks); + memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); + // TODO: figure out it + int num_to_keep = 0; + for (int i = 0; i < polys_num; i++) { + int nblock = i / threadsPerBlock; + int inblock = i % threadsPerBlock; + + if (!(remv[nblock] & (1ULL << inblock))) { + keep_out[num_to_keep++] = i; + unsigned long long *p = &mask_host[0] + i * col_blocks; + for (int j = nblock; j < col_blocks; j++) { + remv[j] |= p[j]; + } + } + } + *num_out = num_to_keep; + + CUDA_CHECK(cudaFree(polys_dev)); + CUDA_CHECK(cudaFree(mask_dev)); +} + +// +//int main(){ +// double p[8] = {0, 0, 1, 0, 1, 1, 0, 1}; +// double q[8] = {0.5, 0.5, 1.5, 0.5, 1.5, 1.5, 0.5, 1.5}; +// vector P(p, p + 8); +// vector Q(q, q + 8); +// iou_poly(P, Q); +// return 0; +//} + +//int main(){ +// double p[8] = {0, 0, 1, 0, 1, 1, 0, 1}; +// double q[8] = {0.5, 0.5, 1.5, 0.5, 1.5, 1.5, 0.5, 1.5}; +// iou_poly(p, q); +// return 0; +//} \ No newline at end of file diff --git a/utils/poly_nms_gpu/poly_nms_test.py b/utils/poly_nms_gpu/poly_nms_test.py new file mode 100644 index 00000000..e69de29b diff --git a/utils/poly_nms_gpu/poly_overlaps.hpp b/utils/poly_nms_gpu/poly_overlaps.hpp new file mode 100644 index 00000000..1be3e9b4 --- /dev/null +++ b/utils/poly_nms_gpu/poly_overlaps.hpp @@ -0,0 +1 @@ +void _overlaps(float* overlaps,const float* boxes,const float* query_boxes, int n, int k, int device_id); diff --git a/utils/poly_nms_gpu/poly_overlaps.pyx b/utils/poly_nms_gpu/poly_overlaps.pyx new file mode 100644 index 00000000..14b08cd3 --- /dev/null +++ b/utils/poly_nms_gpu/poly_overlaps.pyx @@ -0,0 +1,14 @@ +import numpy as np +cimport numpy as np + +cdef extern from "poly_overlaps.hpp": + void _overlaps(np.float32_t*, np.float32_t*, np.float32_t*, int, int, int) + +def poly_overlaps (np.ndarray[np.float32_t, ndim=2] boxes, np.ndarray[np.float32_t, ndim=2] query_boxes, np.int32_t device_id=0): + cdef int N = boxes.shape[0] + cdef int K = query_boxes.shape[0] + cdef np.ndarray[np.float32_t, ndim=2] overlaps = np.zeros((N, K), dtype = np.float32) + _overlaps(&overlaps[0, 0], &boxes[0, 0], &query_boxes[0, 0], N, K, device_id) + return overlaps + + diff --git a/utils/poly_nms_gpu/poly_overlaps_kernel.cu b/utils/poly_nms_gpu/poly_overlaps_kernel.cu new file mode 100644 index 00000000..bf236259 --- /dev/null +++ b/utils/poly_nms_gpu/poly_overlaps_kernel.cu @@ -0,0 +1,427 @@ + +#include "poly_overlaps.hpp" +#include +#include +#include +#include +#include + +using namespace std; + +//##define CUDA_CHECK(condition)\ +// +// do { +// cudaError_t error = condition; +// if (error != cudaSuccess) { +// +// } +// } + +#define CUDA_CHECK(condition) \ + /* Code block avoids redefinition of cudaError_t error */ \ + do { \ + cudaError_t error = condition; \ + if (error != cudaSuccess) { \ + std::cout << cudaGetErrorString(error) << std::endl; \ + } \ + } while (0) + +#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0)) +int const threadsPerBlock = sizeof(unsigned long long) * 8; + + +#define maxn 510 +const double eps=1E-8; + +__device__ inline int sig(float d){ + return(d>eps)-(d<-eps); +} +// struct Point{ +// double x,y; Point(){} +// Point(double x,double y):x(x),y(y){} +// bool operator==(const Point&p)const{ +// return sig(x-p.x)==0&&sig(y-p.y)==0; +// } +// }; + +__device__ inline int point_eq(const float2 a, const float2 b) { + return (sig(a.x - b.x) == 0) && (sig(a.y - b.y)==0); +} + +__device__ inline void point_swap(float2 *a, float2 *b) { + float2 temp = *a; + *a = *b; + *b = temp; +} + +__device__ inline void point_reverse(float2 *first, float2* last) +{ + while ((first!=last)&&(first!=--last)) { + point_swap (first,last); + ++first; + } +} +// void point_reverse(Point* first, Point* last) +// { +// while ((first!=last)&&(first!=--last)) { +// point_swap (first,last); +// ++first; +// } +// } + + +__device__ inline float cross(float2 o,float2 a,float2 b){ //叉积 + return(a.x-o.x)*(b.y-o.y)-(b.x-o.x)*(a.y-o.y); +} +__device__ inline float area(float2* ps,int n){ + ps[n]=ps[0]; + float res=0; + for(int i=0;i0) pp[m++]=p[i]; +// if(sig(cross(a,b,p[i]))!=sig(cross(a,b,p[i+1]))) +// lineCross(a,b,p[i],p[i+1],pp[m++]); +// } +// n=0; + +// for(int i=0;i1&&p[n-1]==p[0])n--; +// while(n>1&&point_eq(p[n-1], p[0]))n--; +// // int x = blockIdx.x * blockDim.x + threadIdx.x; +// // // corresponding to k +// // int y = blockIdx.y * blockDim.y + threadIdx.y; +// // int offset = x * 1 + y; +// // printf("polygon_cut, offset\n"); +// } + +__device__ inline void polygon_cut(float2*p,int&n,float2 a,float2 b, float2* pp){ + // TODO: The static variable may be the reason, why single thread is ok, multiple threads are not work + // printf("polygon_cut, offset\n"); + + // static float2 pp[maxn]; + int m=0;p[n]=p[0]; + for(int i=0;i0) pp[m++]=p[i]; + if(sig(cross(a,b,p[i]))!=sig(cross(a,b,p[i+1]))) + lineCross(a,b,p[i],p[i+1],pp[m++]); + } + n=0; + + for(int i=0;i1&&p[n-1]==p[0])n--; + while(n>1&&point_eq(p[n-1], p[0]))n--; + // int x = blockIdx.x * blockDim.x + threadIdx.x; + // // corresponding to k + // int y = blockIdx.y * blockDim.y + threadIdx.y; + // int offset = x * 1 + y; + // printf("polygon_cut, offset\n"); +} + +//---------------华丽的分隔线-----------------// +//返回三角形oab和三角形ocd的有向交面积,o是原点// +__device__ inline float intersectArea(float2 a,float2 b,float2 c,float2 d){ + float2 o = make_float2(0,0); + int s1=sig(cross(o,a,b)); + int s2=sig(cross(o,c,d)); + if(s1==0||s2==0)return 0.0;//退化，面积为0 + // if(s1==-1) swap(a,b); + // if(s2==-1) swap(c,d); + // printf("before swap\n"); + // printf("a.x %f, a.y %f\n", a.x, a.y); + // printf("b.x %f, b.y %f\n", b.x, b.y); + if(s1 == -1) point_swap(&a, &b); + // printf("a.x %f, a.y %f\n", a.x, a.y); + // printf("b.x %f, b.y %f\n", b.x, b.y); + // printf("after swap\n"); + if(s2 == -1) point_swap(&c, &d); + float2 p[10]={o,a,b}; + int n=3; + + // // manually implement polygon_cut(p, n, a, b) + // float2 pp[maxn]; + // // polygon_cut(p, n, o, c) + // int m=0;p[n]=p[0]; + // for(int i=0;i0) pp[m++]=p[i]; + // if(sig(cross(o,c,p[i]))!=sig(cross(o,c,p[i+1]))) + // lineCross(o,c,p[i],p[i+1],pp[m++]); + // } + // n=0; + + // for(int i=0;i1&&point_eq(p[n-1], p[0]))n--; + + // // polygon_cut(p, n, c, d) + // m=0;p[n]=p[0]; + // for(int i=0;i0) pp[m++]=p[i]; + // if(sig(cross(c,d,p[i]))!=sig(cross(c,d,p[i+1]))) + // lineCross(c,d,p[i],p[i+1],pp[m++]); + // } + // n=0; + + // for(int i=0;i1&&point_eq(p[n-1], p[0]))n--; + + // // polygon_cut(p, n, d, o) + // m=0;p[n]=p[0]; + // for(int i=0;i0) pp[m++]=p[i]; + // if(sig(cross(d,o,p[i]))!=sig(cross(d,o,p[i+1]))) + // lineCross(d,o,p[i],p[i+1],pp[m++]); + // } + // n=0; + + // for(int i=0;i1&&point_eq(p[n-1], p[0]))n--; + float2 pp[maxn]; + polygon_cut(p,n,o,c,pp); + polygon_cut(p,n,c,d,pp); + polygon_cut(p,n,d,o,pp); + float res=fabs(area(p,n)); + int x = blockIdx.x * blockDim.x + threadIdx.x; + // corresponding to k + int y = blockIdx.y * blockDim.y + threadIdx.y; + int offset = x * 1 + y; + // printf("intersectArea2, offset: %d, %f, %f, %f, %f, %f, %f, %f, %f, res: %f\n", offset, a.x, a.y, b.x, b.y, c.x, c.y, d.x, d.y, res); + if(s1*s2==-1) res=-res;return res; + +} +//求两多边形的交面积 +// TODO: here changed the input, this need to be debug +__device__ inline float intersectArea(float2*ps1,int n1,float2*ps2,int n2){ + int x = blockIdx.x * blockDim.x + threadIdx.x; + // corresponding to k + int y = blockIdx.y * blockDim.y + threadIdx.y; + int offset = x * 1 + y; + if(area(ps1,n1)<0) point_reverse(ps1,ps1+n1); + if(area(ps2,n2)<0) point_reverse(ps2,ps2+n2); + ps1[n1]=ps1[0]; + ps2[n2]=ps2[0]; + float res=0; + for(int i=0;i p, vector q) { +// Point ps1[maxn],ps2[maxn]; +// int n1 = 4; +// int n2 = 4; +// for (int i = 0; i < 4; i++) { +// ps1[i].x = p[i * 2]; +// ps1[i].y = p[i * 2 + 1]; +// +// ps2[i].x = q[i * 2]; +// ps2[i].y = q[i * 2 + 1]; +// } +// double inter_area = intersectArea(ps1, n1, ps2, n2); +// double union_area = fabs(area(ps1, n1)) + fabs(area(ps2, n2)) - inter_area; +// double iou = inter_area / union_area; +// +//// cout << "inter_area:" << inter_area << endl; +//// cout << "union_area:" << union_area << endl; +//// cout << "iou:" << iou << endl; +// +// return iou; +//} + +__device__ inline void RotBox2Poly(float const * const dbox, float2 * ps) { + float cs = cos(dbox[4]); + float ss = sin(dbox[4]); + float w = dbox[2]; + float h = dbox[3]; + + float x_ctr = dbox[0]; + float y_ctr = dbox[1]; + ps[0].x = x_ctr + cs * (w / 2.0) - ss * (-h / 2.0); + ps[1].x = x_ctr + cs * (w / 2.0) - ss * (h / 2.0); + ps[2].x = x_ctr + cs * (-w / 2.0) - ss * (h / 2.0); + ps[3].x = x_ctr + cs * (-w / 2.0) - ss * (-h / 2.0); + + ps[0].y = y_ctr + ss * (w / 2.0) + cs * (-h / 2.0); + ps[1].y = y_ctr + ss * (w / 2.0) + cs * (h / 2.0); + ps[2].y = y_ctr + ss * (-w / 2.0) + cs * (h / 2.0); + ps[3].y = y_ctr + ss * (-w / 2.0) + cs * (-h / 2.0); +} + + +__device__ inline float devPolyIoU(float const * const dbbox1, float const * const dbbox2) { + + + float2 ps1[maxn], ps2[maxn]; + int n1 = 4; + int n2 = 4; + + + + + RotBox2Poly(dbbox1, ps1); + RotBox2Poly(dbbox2, ps2); + + // printf("ps1: %f, %f, %f, %f, %f, %f, %f, %f\n", ps1[0].x, ps1[0].y, ps1[1].x, ps1[1].y, ps1[2].x, ps1[2].y, ps1[3].x, ps1[3].y); + // printf("ps2: %f, %f, %f, %f, %f, %f, %f, %f\n", ps2[0].x, ps2[0].y, ps2[1].x, ps2[1].y, ps2[2].x, ps2[2].y, ps2[3].x, ps2[3].y); + float inter_area = intersectArea(ps1, n1, ps2, n2); + //printf("inter_area: %f \n", inter_area); + float union_area = fabs(area(ps1, n1)) + fabs(area(ps2, n2)) - inter_area; + //printf("before union_area\n"); + //printf("union_area: %f \n", union_area); + float iou = 0; + if (union_area == 0) { + iou = (inter_area + 1) / (union_area + 1); + } else { + iou = inter_area / union_area; + } + // printf("iou: %f \n", iou); + return iou; +} + +__global__ void overlaps_kernel(const int N, const int K, const float* dev_boxes, + const float * dev_query_boxes, float* dev_overlaps) { + +// const int col_start = blockIdx.y; +// const int row_start = blockIdx.x; + + // corresponding to n + int x = blockIdx.x * blockDim.x + threadIdx.x; + // corresponding to k + int y = blockIdx.y * blockDim.y + threadIdx.y; + if ((x < N) && (y < K)) { + int offset = x * K + y; + + //printf + // printf("offset: %d dbbox: %f %f %f %f %f\n", offset, (dev_boxes + x*5)[0], + // (dev_boxes + x*5)[1], (dev_boxes + x*5)[2], (dev_boxes + x*5)[3], + // (dev_boxes + x*5)[4] ); + // printf("offset: %d dbbox: %f %f %f %f %f\n", offset, (dev_query_boxes + y*5)[0], + // (dev_query_boxes + y*5)[1], (dev_query_boxes + y*5)[2], (dev_query_boxes + y*5)[3], + // (dev_query_boxes + y*5)[4] ); + + dev_overlaps[offset] = devPolyIoU(dev_boxes + x * 5, dev_query_boxes + y * 5); + } +} + + +void _set_device(int device_id) { + int current_device; + CUDA_CHECK(cudaGetDevice(¤t_device)); + if (current_device == device_id) { + return; + } + // The call to cudaSetDevice must come before any calls to Get, which + // may perform initialization using the GPU. + CUDA_CHECK(cudaSetDevice(device_id)); +} + + +void _overlaps(float* overlaps,const float* boxes,const float* query_boxes, int n, int k, int device_id) { + + _set_device(device_id); + + float* overlaps_dev = NULL; + float* boxes_dev = NULL; + float* query_boxes_dev = NULL; + + + CUDA_CHECK(cudaMalloc(&boxes_dev, + n * 5 * sizeof(float))); + + + + CUDA_CHECK(cudaMemcpy(boxes_dev, + boxes, + n * 5 * sizeof(float), + cudaMemcpyHostToDevice)); + + + + CUDA_CHECK(cudaMalloc(&query_boxes_dev, + k * 5 * sizeof(float))); + + + + CUDA_CHECK(cudaMemcpy(query_boxes_dev, + query_boxes, + k * 5 * sizeof(float), + cudaMemcpyHostToDevice)); + + CUDA_CHECK(cudaMalloc(&overlaps_dev, + n * k * sizeof(float))); + + + if (true){} + + + dim3 blocks(DIVUP(n, 32), + DIVUP(k, 32)); + + dim3 threads(32, 32); + + + overlaps_kernel<<>>(n, k, + boxes_dev, + query_boxes_dev, + overlaps_dev); + + CUDA_CHECK(cudaMemcpy(overlaps, + overlaps_dev, + n * k * sizeof(float), + cudaMemcpyDeviceToHost)); + + + CUDA_CHECK(cudaFree(overlaps_dev)); + CUDA_CHECK(cudaFree(boxes_dev)); + CUDA_CHECK(cudaFree(query_boxes_dev)); + +} diff --git a/utils/poly_nms_gpu/setup.py b/utils/poly_nms_gpu/setup.py new file mode 100644 index 00000000..7a0b2501 --- /dev/null +++ b/utils/poly_nms_gpu/setup.py @@ -0,0 +1,152 @@ +""" + setup.py file for SWIG example +""" +import os +from os.path import join as pjoin +from setuptools import setup +from distutils.extension import Extension +from Cython.Distutils import build_ext +import subprocess +import numpy as np + +def find_in_path(name, path): + "Find a file in a search path" + # Adapted fom + # http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/ + for dir in path.split(os.pathsep): + binpath = pjoin(dir, name) + if os.path.exists(binpath): + return os.path.abspath(binpath) + return None + + +def locate_cuda(): + """Locate the CUDA environment on the system + + Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64' + and values giving the absolute path to each directory. + + Starts by looking for the CUDAHOME env variable. If not found, everything + is based on finding 'nvcc' in the PATH. + """ + + # first check if the CUDAHOME env variable is in use + if 'CUDAHOME' in os.environ: + home = os.environ['CUDAHOME'] + nvcc = pjoin(home, 'bin', 'nvcc') + else: + # otherwise, search the PATH for NVCC + default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin') + nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path) + if nvcc is None: + raise EnvironmentError('The nvcc binary could not be ' + 'located in your $PATH. Either add it to your path, or set $CUDAHOME') + home = os.path.dirname(os.path.dirname(nvcc)) + + cudaconfig = {'home':home, 'nvcc':nvcc, + 'include': pjoin(home, 'include'), + 'lib64': pjoin(home, 'lib64')} + try: + for k, v in cudaconfig.iteritems(): + if not os.path.exists(v): + raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v)) + except: + for k, v in cudaconfig.items(): + if not os.path.exists(v): + raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v)) + return cudaconfig +CUDA = locate_cuda() + + +# Obtain the numpy include directory. This logic works across numpy versions. +try: + numpy_include = np.get_include() +except AttributeError: + numpy_include = np.get_numpy_include() + +def customize_compiler_for_nvcc(self): + """inject deep into distutils to customize how the dispatch + to gcc/nvcc works. + + If you subclass UnixCCompiler, it's not trivial to get your subclass + injected in, and still have the right customizations (i.e. + distutils.sysconfig.customize_compiler) run on it. So instead of going + the OO route, I have this. Note, it's kindof like a wierd functional + subclassing going on.""" + + # tell the compiler it can processes .cu + self.src_extensions.append('.cu') + + # save references to the default compiler_so and _comple methods + default_compiler_so = self.compiler_so + super = self._compile + + # now redefine the _compile method. This gets executed for each + # object but distutils doesn't have the ability to change compilers + # based on source extension: we add it. + def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts): + if os.path.splitext(src)[1] == '.cu': + # use the cuda for .cu files + self.set_executable('compiler_so', CUDA['nvcc']) + # use only a subset of the extra_postargs, which are 1-1 translated + # from the extra_compile_args in the Extension class + postargs = extra_postargs['nvcc'] + else: + postargs = extra_postargs['gcc'] + + super(obj, src, ext, cc_args, postargs, pp_opts) + # reset the default compiler_so, which we might have changed for cuda + self.compiler_so = default_compiler_so + + # inject our redefined _compile method into the class + self._compile = _compile + + +# run the customize_compiler +class custom_build_ext(build_ext): + def build_extensions(self): + customize_compiler_for_nvcc(self.compiler) + build_ext.build_extensions(self) + +ext_modules = [ + Extension('poly_nms', + ['poly_nms_kernel.cu', 'poly_nms.pyx'], + library_dirs=[CUDA['lib64']], + libraries=['cudart'], + language='c++', + runtime_library_dirs=[CUDA['lib64']], + # this syntax is specific to this build system + # we're only going to use certain compiler args with nvcc and not with + # gcc the implementation of this trick is in customize_compiler() below + extra_compile_args={'gcc': ["-Wno-unused-function"], + 'nvcc': ['-arch=sm_35', + '--ptxas-options=-v', + '-c', + '--compiler-options', + "'-fPIC'"]}, + include_dirs=[numpy_include, CUDA['include']] + ), + Extension('poly_overlaps', + ['poly_overlaps_kernel.cu', 'poly_overlaps.pyx'], + library_dirs=[CUDA['lib64']], + libraries=['cudart'], + language='c++', + runtime_library_dirs=[CUDA['lib64']], + # this syntax is specific to this build system + # we're only going to use certain compiler args with nvcc and not with + # gcc the implementation of this trick is in customize_compiler() below + extra_compile_args={'gcc': ["-Wno-unused-function"], + 'nvcc': ['-arch=sm_35', + '--ptxas-options=-v', + '-c', + '--compiler-options', + "'-fPIC'"]}, + include_dirs=[numpy_include, CUDA['include']] + ), +] +setup( + name='rotation', + ext_modules=ext_modules, + # inject our custom trigger + cmdclass={'build_ext': custom_build_ext}, +) diff --git a/utils/polyiou.cpp b/utils/polyiou.cpp new file mode 100644 index 00000000..fe182c28 --- /dev/null +++ b/utils/polyiou.cpp @@ -0,0 +1,144 @@ + +#include +#include +#include +#include +#include +using namespace std; +#define maxn 51 +const double eps=1E-8; +int sig(double d){ + return(d>eps)-(d<-eps); +} +struct Point{ + double x,y; Point(){} + Point(double x,double y):x(x),y(y){} + bool operator==(const Point&p)const{ + return sig(x-p.x)==0&&sig(y-p.y)==0; + } +}; +double cross(Point o,Point a,Point b){//叉积 + return(a.x-o.x)*(b.y-o.y)-(b.x-o.x)*(a.y-o.y); +} +double area(Point* ps,int n){ + ps[n]=ps[0]; + double res=0; + for(int i=0;i0) pp[m++]=p[i]; +// if(sig(cross(a,b,p[i]))!=sig(cross(a,b,p[i+1]))) +// lineCross(a,b,p[i],p[i+1],pp[m++]); +// } +// n=0; +// for(int i=0;i1&&p[n-1]==p[0])n--; +//} +void polygon_cut(Point*p,int&n,Point a,Point b, Point* pp){ +// static Point pp[maxn]; + int m=0;p[n]=p[0]; + for(int i=0;i0) pp[m++]=p[i]; + if(sig(cross(a,b,p[i]))!=sig(cross(a,b,p[i+1]))) + lineCross(a,b,p[i],p[i+1],pp[m++]); + } + n=0; + for(int i=0;i1&&p[n-1]==p[0])n--; +} +//---------------华丽的分隔线-----------------// +//返回三角形oab和三角形ocd的有向交面积,o是原点// +double intersectArea(Point a,Point b,Point c,Point d){ + Point o(0,0); + int s1=sig(cross(o,a,b)); + int s2=sig(cross(o,c,d)); + if(s1==0||s2==0)return 0.0;//退化，面积为0 + if(s1==-1) swap(a,b); + if(s2==-1) swap(c,d); + Point p[10]={o,a,b}; + int n=3; + Point pp[maxn]; + polygon_cut(p,n,o,c, pp); + polygon_cut(p,n,c,d, pp); + polygon_cut(p,n,d,o, pp); + double res=fabs(area(p,n)); + if(s1*s2==-1) res=-res;return res; +} +//求两多边形的交面积 +double intersectArea(Point*ps1,int n1,Point*ps2,int n2){ + if(area(ps1,n1)<0) reverse(ps1,ps1+n1); + if(area(ps2,n2)<0) reverse(ps2,ps2+n2); + ps1[n1]=ps1[0]; + ps2[n2]=ps2[0]; + double res=0; + for(int i=0;i p, vector q) { + Point ps1[maxn],ps2[maxn]; + int n1 = 4; + int n2 = 4; + for (int i = 0; i < 4; i++) { + ps1[i].x = p[i * 2]; + ps1[i].y = p[i * 2 + 1]; + + ps2[i].x = q[i * 2]; + ps2[i].y = q[i * 2 + 1]; + } + double inter_area = intersectArea(ps1, n1, ps2, n2); + double union_area = fabs(area(ps1, n1)) + fabs(area(ps2, n2)) - inter_area; + double iou = inter_area / union_area; + +// cout << "inter_area:" << inter_area << endl; +// cout << "union_area:" << union_area << endl; +// cout << "iou:" << iou << endl; + + return iou; +} +// +//int main(){ +// double p[8] = {0, 0, 1, 0, 1, 1, 0, 1}; +// double q[8] = {0.5, 0.5, 1.5, 0.5, 1.5, 1.5, 0.5, 1.5}; +// vector P(p, p + 8); +// vector Q(q, q + 8); +// iou_poly(P, Q); +// return 0; +//} + +//int main(){ +// double p[8] = {0, 0, 1, 0, 1, 1, 0, 1}; +// double q[8] = {0.5, 0.5, 1.5, 0.5, 1.5, 1.5, 0.5, 1.5}; +// iou_poly(p, q); +// return 0; +//} \ No newline at end of file diff --git a/utils/polyiou.h b/utils/polyiou.h new file mode 100644 index 00000000..dca2679d --- /dev/null +++ b/utils/polyiou.h @@ -0,0 +1,10 @@ +// +// Created by dingjian on 18-2-3. +// + +#ifndef POLYIOU_POLYIOU_H +#define POLYIOU_POLYIOU_H + +#include +double iou_poly(std::vector p, std::vector q); +#endif //POLYIOU_POLYIOU_H diff --git a/utils/polyiou.i b/utils/polyiou.i new file mode 100644 index 00000000..3bf82524 --- /dev/null +++ b/utils/polyiou.i @@ -0,0 +1,19 @@ +%module polyiou +%include "std_vector.i" + +namespace std { + %template(VectorDouble) vector; +}; + +%{ +#define SWIG_FILE_WITH_INIT +#include +#include +#include +#include + +#include "polyiou.h" +%} + +%include "polyiou.h" + diff --git a/utils/polyiou.py b/utils/polyiou.py new file mode 100644 index 00000000..00ff7464 --- /dev/null +++ b/utils/polyiou.py @@ -0,0 +1,276 @@ +# This file was automatically generated by SWIG (http://www.swig.org). +# Version 3.0.8 +# +# Do not make changes to this file unless you know what you are doing--modify +# the SWIG interface file instead. + + + + + +from sys import version_info +if version_info >= (2, 6, 0): + def swig_import_helper(): + from os.path import dirname + import imp + fp = None + try: + fp, pathname, description = imp.find_module('_polyiou', [dirname(__file__)]) + except ImportError: + import _polyiou + return _polyiou + if fp is not None: + try: + _mod = imp.load_module('_polyiou', fp, pathname, description) + finally: + fp.close() + return _mod + _polyiou = swig_import_helper() + del swig_import_helper +else: + import _polyiou +del version_info +try: + _swig_property = property +except NameError: + pass # Python < 2.2 doesn't have 'property'. + + +def _swig_setattr_nondynamic(self, class_type, name, value, static=1): + if (name == "thisown"): + return self.this.own(value) + if (name == "this"): + if type(value).__name__ == 'SwigPyObject': + self.__dict__[name] = value + return + method = class_type.__swig_setmethods__.get(name, None) + if method: + return method(self, value) + if (not static): + if _newclass: + object.__setattr__(self, name, value) + else: + self.__dict__[name] = value + else: + raise AttributeError("You cannot add attributes to %s" % self) + + +def _swig_setattr(self, class_type, name, value): + return _swig_setattr_nondynamic(self, class_type, name, value, 0) + + +def _swig_getattr_nondynamic(self, class_type, name, static=1): + if (name == "thisown"): + return self.this.own() + method = class_type.__swig_getmethods__.get(name, None) + if method: + return method(self) + if (not static): + return object.__getattr__(self, name) + else: + raise AttributeError(name) + +def _swig_getattr(self, class_type, name): + return _swig_getattr_nondynamic(self, class_type, name, 0) + + +def _swig_repr(self): + try: + strthis = "proxy of " + self.this.__repr__() + except Exception: + strthis = "" + return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) + +try: + _object = object + _newclass = 1 +except AttributeError: + class _object: + pass + _newclass = 0 + + +class SwigPyIterator(_object): + __swig_setmethods__ = {} + __setattr__ = lambda self, name, value: _swig_setattr(self, SwigPyIterator, name, value) + __swig_getmethods__ = {} + __getattr__ = lambda self, name: _swig_getattr(self, SwigPyIterator, name) + + def __init__(self, *args, **kwargs): + raise AttributeError("No constructor defined - class is abstract") + __repr__ = _swig_repr + __swig_destroy__ = _polyiou.delete_SwigPyIterator + __del__ = lambda self: None + + def value(self): + return _polyiou.SwigPyIterator_value(self) + + def incr(self, n=1): + return _polyiou.SwigPyIterator_incr(self, n) + + def decr(self, n=1): + return _polyiou.SwigPyIterator_decr(self, n) + + def distance(self, x): + return _polyiou.SwigPyIterator_distance(self, x) + + def equal(self, x): + return _polyiou.SwigPyIterator_equal(self, x) + + def copy(self): + return _polyiou.SwigPyIterator_copy(self) + + def next(self): + return _polyiou.SwigPyIterator_next(self) + + def __next__(self): + return _polyiou.SwigPyIterator___next__(self) + + def previous(self): + return _polyiou.SwigPyIterator_previous(self) + + def advance(self, n): + return _polyiou.SwigPyIterator_advance(self, n) + + def __eq__(self, x): + return _polyiou.SwigPyIterator___eq__(self, x) + + def __ne__(self, x): + return _polyiou.SwigPyIterator___ne__(self, x) + + def __iadd__(self, n): + return _polyiou.SwigPyIterator___iadd__(self, n) + + def __isub__(self, n): + return _polyiou.SwigPyIterator___isub__(self, n) + + def __add__(self, n): + return _polyiou.SwigPyIterator___add__(self, n) + + def __sub__(self, *args): + return _polyiou.SwigPyIterator___sub__(self, *args) + def __iter__(self): + return self +SwigPyIterator_swigregister = _polyiou.SwigPyIterator_swigregister +SwigPyIterator_swigregister(SwigPyIterator) + +class VectorDouble(_object): + __swig_setmethods__ = {} + __setattr__ = lambda self, name, value: _swig_setattr(self, VectorDouble, name, value) + __swig_getmethods__ = {} + __getattr__ = lambda self, name: _swig_getattr(self, VectorDouble, name) + __repr__ = _swig_repr + + def iterator(self): + return _polyiou.VectorDouble_iterator(self) + def __iter__(self): + return self.iterator() + + def __nonzero__(self): + return _polyiou.VectorDouble___nonzero__(self) + + def __bool__(self): + return _polyiou.VectorDouble___bool__(self) + + def __len__(self): + return _polyiou.VectorDouble___len__(self) + + def __getslice__(self, i, j): + return _polyiou.VectorDouble___getslice__(self, i, j) + + def __setslice__(self, *args): + return _polyiou.VectorDouble___setslice__(self, *args) + + def __delslice__(self, i, j): + return _polyiou.VectorDouble___delslice__(self, i, j) + + def __delitem__(self, *args): + return _polyiou.VectorDouble___delitem__(self, *args) + + def __getitem__(self, *args): + return _polyiou.VectorDouble___getitem__(self, *args) + + def __setitem__(self, *args): + return _polyiou.VectorDouble___setitem__(self, *args) + + def pop(self): + return _polyiou.VectorDouble_pop(self) + + def append(self, x): + return _polyiou.VectorDouble_append(self, x) + + def empty(self): + return _polyiou.VectorDouble_empty(self) + + def size(self): + return _polyiou.VectorDouble_size(self) + + def swap(self, v): + return _polyiou.VectorDouble_swap(self, v) + + def begin(self): + return _polyiou.VectorDouble_begin(self) + + def end(self): + return _polyiou.VectorDouble_end(self) + + def rbegin(self): + return _polyiou.VectorDouble_rbegin(self) + + def rend(self): + return _polyiou.VectorDouble_rend(self) + + def clear(self): + return _polyiou.VectorDouble_clear(self) + + def get_allocator(self): + return _polyiou.VectorDouble_get_allocator(self) + + def pop_back(self): + return _polyiou.VectorDouble_pop_back(self) + + def erase(self, *args): + return _polyiou.VectorDouble_erase(self, *args) + + def __init__(self, *args): + this = _polyiou.new_VectorDouble(*args) + try: + self.this.append(this) + except Exception: + self.this = this + + def push_back(self, x): + return _polyiou.VectorDouble_push_back(self, x) + + def front(self): + return _polyiou.VectorDouble_front(self) + + def back(self): + return _polyiou.VectorDouble_back(self) + + def assign(self, n, x): + return _polyiou.VectorDouble_assign(self, n, x) + + def resize(self, *args): + return _polyiou.VectorDouble_resize(self, *args) + + def insert(self, *args): + return _polyiou.VectorDouble_insert(self, *args) + + def reserve(self, n): + return _polyiou.VectorDouble_reserve(self, n) + + def capacity(self): + return _polyiou.VectorDouble_capacity(self) + __swig_destroy__ = _polyiou.delete_VectorDouble + __del__ = lambda self: None +VectorDouble_swigregister = _polyiou.VectorDouble_swigregister +VectorDouble_swigregister(VectorDouble) + + +def iou_poly(p, q): + return _polyiou.iou_poly(p, q) +iou_poly = _polyiou.iou_poly +# This file is compatible with both classic and new-style classes. + + diff --git a/utils/polyiou_wrap.cxx b/utils/polyiou_wrap.cxx new file mode 100644 index 00000000..b4a034ac --- /dev/null +++ b/utils/polyiou_wrap.cxx @@ -0,0 +1,8369 @@ +/* ---------------------------------------------------------------------------- + * This file was automatically generated by SWIG (http://www.swig.org). + * Version 3.0.8 + * + * This file is not intended to be easily readable and contains a number of + * coding conventions designed to improve portability and efficiency. Do not make + * changes to this file unless you know what you are doing--modify the SWIG + * interface file instead. + * ----------------------------------------------------------------------------- */ + + +#ifndef SWIGPYTHON +#define SWIGPYTHON +#endif + +#define SWIG_PYTHON_DIRECTOR_NO_VTABLE + + +#ifdef __cplusplus +/* SwigValueWrapper is described in swig.swg */ +template class SwigValueWrapper { + struct SwigMovePointer { + T *ptr; + SwigMovePointer(T *p) : ptr(p) { } + ~SwigMovePointer() { delete ptr; } + SwigMovePointer& operator=(SwigMovePointer& rhs) { T* oldptr = ptr; ptr = 0; delete oldptr; ptr = rhs.ptr; rhs.ptr = 0; return *this; } + } pointer; + SwigValueWrapper& operator=(const SwigValueWrapper& rhs); + SwigValueWrapper(const SwigValueWrapper& rhs); +public: + SwigValueWrapper() : pointer(0) { } + SwigValueWrapper& operator=(const T& t) { SwigMovePointer tmp(new T(t)); pointer = tmp; return *this; } + operator T&() const { return *pointer.ptr; } + T *operator&() { return pointer.ptr; } +}; + +template T SwigValueInit() { + return T(); +} +#endif + +/* ----------------------------------------------------------------------------- + * This section contains generic SWIG labels for method/variable + * declarations/attributes, and other compiler dependent labels. + * ----------------------------------------------------------------------------- */ + +/* template workaround for compilers that cannot correctly implement the C++ standard */ +#ifndef SWIGTEMPLATEDISAMBIGUATOR +# if defined(__SUNPRO_CC) && (__SUNPRO_CC <= 0x560) +# define SWIGTEMPLATEDISAMBIGUATOR template +# elif defined(__HP_aCC) +/* Needed even with `aCC -AA' when `aCC -V' reports HP ANSI C++ B3910B A.03.55 */ +/* If we find a maximum version that requires this, the test would be __HP_aCC <= 35500 for A.03.55 */ +# define SWIGTEMPLATEDISAMBIGUATOR template +# else +# define SWIGTEMPLATEDISAMBIGUATOR +# endif +#endif + +/* inline attribute */ +#ifndef SWIGINLINE +# if defined(__cplusplus) || (defined(__GNUC__) && !defined(__STRICT_ANSI__)) +# define SWIGINLINE inline +# else +# define SWIGINLINE +# endif +#endif + +/* attribute recognised by some compilers to avoid 'unused' warnings */ +#ifndef SWIGUNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define SWIGUNUSED __attribute__ ((__unused__)) +# else +# define SWIGUNUSED +# endif +# elif defined(__ICC) +# define SWIGUNUSED __attribute__ ((__unused__)) +# else +# define SWIGUNUSED +# endif +#endif + +#ifndef SWIG_MSC_UNSUPPRESS_4505 +# if defined(_MSC_VER) +# pragma warning(disable : 4505) /* unreferenced local function has been removed */ +# endif +#endif + +#ifndef SWIGUNUSEDPARM +# ifdef __cplusplus +# define SWIGUNUSEDPARM(p) +# else +# define SWIGUNUSEDPARM(p) p SWIGUNUSED +# endif +#endif + +/* internal SWIG method */ +#ifndef SWIGINTERN +# define SWIGINTERN static SWIGUNUSED +#endif + +/* internal inline SWIG method */ +#ifndef SWIGINTERNINLINE +# define SWIGINTERNINLINE SWIGINTERN SWIGINLINE +#endif + +/* exporting methods */ +#if (__GNUC__ >= 4) || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4) +# ifndef GCC_HASCLASSVISIBILITY +# define GCC_HASCLASSVISIBILITY +# endif +#endif + +#ifndef SWIGEXPORT +# if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) +# if defined(STATIC_LINKED) +# define SWIGEXPORT +# else +# define SWIGEXPORT __declspec(dllexport) +# endif +# else +# if defined(__GNUC__) && defined(GCC_HASCLASSVISIBILITY) +# define SWIGEXPORT __attribute__ ((visibility("default"))) +# else +# define SWIGEXPORT +# endif +# endif +#endif + +/* calling conventions for Windows */ +#ifndef SWIGSTDCALL +# if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) +# define SWIGSTDCALL __stdcall +# else +# define SWIGSTDCALL +# endif +#endif + +/* Deal with Microsoft's attempt at deprecating C standard runtime functions */ +#if !defined(SWIG_NO_CRT_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_CRT_SECURE_NO_DEPRECATE) +# define _CRT_SECURE_NO_DEPRECATE +#endif + +/* Deal with Microsoft's attempt at deprecating methods in the standard C++ library */ +#if !defined(SWIG_NO_SCL_SECURE_NO_DEPRECATE) && defined(_MSC_VER) && !defined(_SCL_SECURE_NO_DEPRECATE) +# define _SCL_SECURE_NO_DEPRECATE +#endif + +/* Deal with Apple's deprecated 'AssertMacros.h' from Carbon-framework */ +#if defined(__APPLE__) && !defined(__ASSERT_MACROS_DEFINE_VERSIONS_WITHOUT_UNDERSCORES) +# define __ASSERT_MACROS_DEFINE_VERSIONS_WITHOUT_UNDERSCORES 0 +#endif + +/* Intel's compiler complains if a variable which was never initialised is + * cast to void, which is a common idiom which we use to indicate that we + * are aware a variable isn't used. So we just silence that warning. + * See: https://github.com/swig/swig/issues/192 for more discussion. + */ +#ifdef __INTEL_COMPILER +# pragma warning disable 592 +#endif + + +#if defined(_DEBUG) && defined(SWIG_PYTHON_INTERPRETER_NO_DEBUG) +/* Use debug wrappers with the Python release dll */ +# undef _DEBUG +# include +# define _DEBUG +#else +# include +#endif + +/* ----------------------------------------------------------------------------- + * swigrun.swg + * + * This file contains generic C API SWIG runtime support for pointer + * type checking. + * ----------------------------------------------------------------------------- */ + +/* This should only be incremented when either the layout of swig_type_info changes, + or for whatever reason, the runtime changes incompatibly */ +#define SWIG_RUNTIME_VERSION "4" + +/* define SWIG_TYPE_TABLE_NAME as "SWIG_TYPE_TABLE" */ +#ifdef SWIG_TYPE_TABLE +# define SWIG_QUOTE_STRING(x) #x +# define SWIG_EXPAND_AND_QUOTE_STRING(x) SWIG_QUOTE_STRING(x) +# define SWIG_TYPE_TABLE_NAME SWIG_EXPAND_AND_QUOTE_STRING(SWIG_TYPE_TABLE) +#else +# define SWIG_TYPE_TABLE_NAME +#endif + +/* + You can use the SWIGRUNTIME and SWIGRUNTIMEINLINE macros for + creating a static or dynamic library from the SWIG runtime code. + In 99.9% of the cases, SWIG just needs to declare them as 'static'. + + But only do this if strictly necessary, ie, if you have problems + with your compiler or suchlike. +*/ + +#ifndef SWIGRUNTIME +# define SWIGRUNTIME SWIGINTERN +#endif + +#ifndef SWIGRUNTIMEINLINE +# define SWIGRUNTIMEINLINE SWIGRUNTIME SWIGINLINE +#endif + +/* Generic buffer size */ +#ifndef SWIG_BUFFER_SIZE +# define SWIG_BUFFER_SIZE 1024 +#endif + +/* Flags for pointer conversions */ +#define SWIG_POINTER_DISOWN 0x1 +#define SWIG_CAST_NEW_MEMORY 0x2 + +/* Flags for new pointer objects */ +#define SWIG_POINTER_OWN 0x1 + + +/* + Flags/methods for returning states. + + The SWIG conversion methods, as ConvertPtr, return an integer + that tells if the conversion was successful or not. And if not, + an error code can be returned (see swigerrors.swg for the codes). + + Use the following macros/flags to set or process the returning + states. + + In old versions of SWIG, code such as the following was usually written: + + if (SWIG_ConvertPtr(obj,vptr,ty.flags) != -1) { + // success code + } else { + //fail code + } + + Now you can be more explicit: + + int res = SWIG_ConvertPtr(obj,vptr,ty.flags); + if (SWIG_IsOK(res)) { + // success code + } else { + // fail code + } + + which is the same really, but now you can also do + + Type *ptr; + int res = SWIG_ConvertPtr(obj,(void **)(&ptr),ty.flags); + if (SWIG_IsOK(res)) { + // success code + if (SWIG_IsNewObj(res) { + ... + delete *ptr; + } else { + ... + } + } else { + // fail code + } + + I.e., now SWIG_ConvertPtr can return new objects and you can + identify the case and take care of the deallocation. Of course that + also requires SWIG_ConvertPtr to return new result values, such as + + int SWIG_ConvertPtr(obj, ptr,...) { + if () { + if () { + *ptr = ; + return SWIG_NEWOBJ; + } else { + *ptr = ; + return SWIG_OLDOBJ; + } + } else { + return SWIG_BADOBJ; + } + } + + Of course, returning the plain '0(success)/-1(fail)' still works, but you can be + more explicit by returning SWIG_BADOBJ, SWIG_ERROR or any of the + SWIG errors code. + + Finally, if the SWIG_CASTRANK_MODE is enabled, the result code + allows to return the 'cast rank', for example, if you have this + + int food(double) + int fooi(int); + + and you call + + food(1) // cast rank '1' (1 -> 1.0) + fooi(1) // cast rank '0' + + just use the SWIG_AddCast()/SWIG_CheckState() +*/ + +#define SWIG_OK (0) +#define SWIG_ERROR (-1) +#define SWIG_IsOK(r) (r >= 0) +#define SWIG_ArgError(r) ((r != SWIG_ERROR) ? r : SWIG_TypeError) + +/* The CastRankLimit says how many bits are used for the cast rank */ +#define SWIG_CASTRANKLIMIT (1 << 8) +/* The NewMask denotes the object was created (using new/malloc) */ +#define SWIG_NEWOBJMASK (SWIG_CASTRANKLIMIT << 1) +/* The TmpMask is for in/out typemaps that use temporal objects */ +#define SWIG_TMPOBJMASK (SWIG_NEWOBJMASK << 1) +/* Simple returning values */ +#define SWIG_BADOBJ (SWIG_ERROR) +#define SWIG_OLDOBJ (SWIG_OK) +#define SWIG_NEWOBJ (SWIG_OK | SWIG_NEWOBJMASK) +#define SWIG_TMPOBJ (SWIG_OK | SWIG_TMPOBJMASK) +/* Check, add and del mask methods */ +#define SWIG_AddNewMask(r) (SWIG_IsOK(r) ? (r | SWIG_NEWOBJMASK) : r) +#define SWIG_DelNewMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_NEWOBJMASK) : r) +#define SWIG_IsNewObj(r) (SWIG_IsOK(r) && (r & SWIG_NEWOBJMASK)) +#define SWIG_AddTmpMask(r) (SWIG_IsOK(r) ? (r | SWIG_TMPOBJMASK) : r) +#define SWIG_DelTmpMask(r) (SWIG_IsOK(r) ? (r & ~SWIG_TMPOBJMASK) : r) +#define SWIG_IsTmpObj(r) (SWIG_IsOK(r) && (r & SWIG_TMPOBJMASK)) + +/* Cast-Rank Mode */ +#if defined(SWIG_CASTRANK_MODE) +# ifndef SWIG_TypeRank +# define SWIG_TypeRank unsigned long +# endif +# ifndef SWIG_MAXCASTRANK /* Default cast allowed */ +# define SWIG_MAXCASTRANK (2) +# endif +# define SWIG_CASTRANKMASK ((SWIG_CASTRANKLIMIT) -1) +# define SWIG_CastRank(r) (r & SWIG_CASTRANKMASK) +SWIGINTERNINLINE int SWIG_AddCast(int r) { + return SWIG_IsOK(r) ? ((SWIG_CastRank(r) < SWIG_MAXCASTRANK) ? (r + 1) : SWIG_ERROR) : r; +} +SWIGINTERNINLINE int SWIG_CheckState(int r) { + return SWIG_IsOK(r) ? SWIG_CastRank(r) + 1 : 0; +} +#else /* no cast-rank mode */ +# define SWIG_AddCast(r) (r) +# define SWIG_CheckState(r) (SWIG_IsOK(r) ? 1 : 0) +#endif + + +#include + +#ifdef __cplusplus +extern "C" { +#endif + +typedef void *(*swig_converter_func)(void *, int *); +typedef struct swig_type_info *(*swig_dycast_func)(void **); + +/* Structure to store information on one type */ +typedef struct swig_type_info { + const char *name; /* mangled name of this type */ + const char *str; /* human readable name of this type */ + swig_dycast_func dcast; /* dynamic cast function down a hierarchy */ + struct swig_cast_info *cast; /* linked list of types that can cast into this type */ + void *clientdata; /* language specific type data */ + int owndata; /* flag if the structure owns the clientdata */ +} swig_type_info; + +/* Structure to store a type and conversion function used for casting */ +typedef struct swig_cast_info { + swig_type_info *type; /* pointer to type that is equivalent to this type */ + swig_converter_func converter; /* function to cast the void pointers */ + struct swig_cast_info *next; /* pointer to next cast in linked list */ + struct swig_cast_info *prev; /* pointer to the previous cast */ +} swig_cast_info; + +/* Structure used to store module information + * Each module generates one structure like this, and the runtime collects + * all of these structures and stores them in a circularly linked list.*/ +typedef struct swig_module_info { + swig_type_info **types; /* Array of pointers to swig_type_info structures that are in this module */ + size_t size; /* Number of types in this module */ + struct swig_module_info *next; /* Pointer to next element in circularly linked list */ + swig_type_info **type_initial; /* Array of initially generated type structures */ + swig_cast_info **cast_initial; /* Array of initially generated casting structures */ + void *clientdata; /* Language specific module data */ +} swig_module_info; + +/* + Compare two type names skipping the space characters, therefore + "char*" == "char *" and "Class" == "Class", etc. + + Return 0 when the two name types are equivalent, as in + strncmp, but skipping ' '. +*/ +SWIGRUNTIME int +SWIG_TypeNameComp(const char *f1, const char *l1, + const char *f2, const char *l2) { + for (;(f1 != l1) && (f2 != l2); ++f1, ++f2) { + while ((*f1 == ' ') && (f1 != l1)) ++f1; + while ((*f2 == ' ') && (f2 != l2)) ++f2; + if (*f1 != *f2) return (*f1 > *f2) ? 1 : -1; + } + return (int)((l1 - f1) - (l2 - f2)); +} + +/* + Check type equivalence in a name list like ||... + Return 0 if equal, -1 if nb < tb, 1 if nb > tb +*/ +SWIGRUNTIME int +SWIG_TypeCmp(const char *nb, const char *tb) { + int equiv = 1; + const char* te = tb + strlen(tb); + const char* ne = nb; + while (equiv != 0 && *ne) { + for (nb = ne; *ne; ++ne) { + if (*ne == '|') break; + } + equiv = SWIG_TypeNameComp(nb, ne, tb, te); + if (*ne) ++ne; + } + return equiv; +} + +/* + Check type equivalence in a name list like ||... + Return 0 if not equal, 1 if equal +*/ +SWIGRUNTIME int +SWIG_TypeEquiv(const char *nb, const char *tb) { + return SWIG_TypeCmp(nb, tb) == 0 ? 1 : 0; +} + +/* + Check the typename +*/ +SWIGRUNTIME swig_cast_info * +SWIG_TypeCheck(const char *c, swig_type_info *ty) { + if (ty) { + swig_cast_info *iter = ty->cast; + while (iter) { + if (strcmp(iter->type->name, c) == 0) { + if (iter == ty->cast) + return iter; + /* Move iter to the top of the linked list */ + iter->prev->next = iter->next; + if (iter->next) + iter->next->prev = iter->prev; + iter->next = ty->cast; + iter->prev = 0; + if (ty->cast) ty->cast->prev = iter; + ty->cast = iter; + return iter; + } + iter = iter->next; + } + } + return 0; +} + +/* + Identical to SWIG_TypeCheck, except strcmp is replaced with a pointer comparison +*/ +SWIGRUNTIME swig_cast_info * +SWIG_TypeCheckStruct(swig_type_info *from, swig_type_info *ty) { + if (ty) { + swig_cast_info *iter = ty->cast; + while (iter) { + if (iter->type == from) { + if (iter == ty->cast) + return iter; + /* Move iter to the top of the linked list */ + iter->prev->next = iter->next; + if (iter->next) + iter->next->prev = iter->prev; + iter->next = ty->cast; + iter->prev = 0; + if (ty->cast) ty->cast->prev = iter; + ty->cast = iter; + return iter; + } + iter = iter->next; + } + } + return 0; +} + +/* + Cast a pointer up an inheritance hierarchy +*/ +SWIGRUNTIMEINLINE void * +SWIG_TypeCast(swig_cast_info *ty, void *ptr, int *newmemory) { + return ((!ty) || (!ty->converter)) ? ptr : (*ty->converter)(ptr, newmemory); +} + +/* + Dynamic pointer casting. Down an inheritance hierarchy +*/ +SWIGRUNTIME swig_type_info * +SWIG_TypeDynamicCast(swig_type_info *ty, void **ptr) { + swig_type_info *lastty = ty; + if (!ty || !ty->dcast) return ty; + while (ty && (ty->dcast)) { + ty = (*ty->dcast)(ptr); + if (ty) lastty = ty; + } + return lastty; +} + +/* + Return the name associated with this type +*/ +SWIGRUNTIMEINLINE const char * +SWIG_TypeName(const swig_type_info *ty) { + return ty->name; +} + +/* + Return the pretty name associated with this type, + that is an unmangled type name in a form presentable to the user. +*/ +SWIGRUNTIME const char * +SWIG_TypePrettyName(const swig_type_info *type) { + /* The "str" field contains the equivalent pretty names of the + type, separated by vertical-bar characters. We choose + to print the last name, as it is often (?) the most + specific. */ + if (!type) return NULL; + if (type->str != NULL) { + const char *last_name = type->str; + const char *s; + for (s = type->str; *s; s++) + if (*s == '|') last_name = s+1; + return last_name; + } + else + return type->name; +} + +/* + Set the clientdata field for a type +*/ +SWIGRUNTIME void +SWIG_TypeClientData(swig_type_info *ti, void *clientdata) { + swig_cast_info *cast = ti->cast; + /* if (ti->clientdata == clientdata) return; */ + ti->clientdata = clientdata; + + while (cast) { + if (!cast->converter) { + swig_type_info *tc = cast->type; + if (!tc->clientdata) { + SWIG_TypeClientData(tc, clientdata); + } + } + cast = cast->next; + } +} +SWIGRUNTIME void +SWIG_TypeNewClientData(swig_type_info *ti, void *clientdata) { + SWIG_TypeClientData(ti, clientdata); + ti->owndata = 1; +} + +/* + Search for a swig_type_info structure only by mangled name + Search is a O(log #types) + + We start searching at module start, and finish searching when start == end. + Note: if start == end at the beginning of the function, we go all the way around + the circular list. +*/ +SWIGRUNTIME swig_type_info * +SWIG_MangledTypeQueryModule(swig_module_info *start, + swig_module_info *end, + const char *name) { + swig_module_info *iter = start; + do { + if (iter->size) { + size_t l = 0; + size_t r = iter->size - 1; + do { + /* since l+r >= 0, we can (>> 1) instead (/ 2) */ + size_t i = (l + r) >> 1; + const char *iname = iter->types[i]->name; + if (iname) { + int compare = strcmp(name, iname); + if (compare == 0) { + return iter->types[i]; + } else if (compare < 0) { + if (i) { + r = i - 1; + } else { + break; + } + } else if (compare > 0) { + l = i + 1; + } + } else { + break; /* should never happen */ + } + } while (l <= r); + } + iter = iter->next; + } while (iter != end); + return 0; +} + +/* + Search for a swig_type_info structure for either a mangled name or a human readable name. + It first searches the mangled names of the types, which is a O(log #types) + If a type is not found it then searches the human readable names, which is O(#types). + + We start searching at module start, and finish searching when start == end. + Note: if start == end at the beginning of the function, we go all the way around + the circular list. +*/ +SWIGRUNTIME swig_type_info * +SWIG_TypeQueryModule(swig_module_info *start, + swig_module_info *end, + const char *name) { + /* STEP 1: Search the name field using binary search */ + swig_type_info *ret = SWIG_MangledTypeQueryModule(start, end, name); + if (ret) { + return ret; + } else { + /* STEP 2: If the type hasn't been found, do a complete search + of the str field (the human readable name) */ + swig_module_info *iter = start; + do { + size_t i = 0; + for (; i < iter->size; ++i) { + if (iter->types[i]->str && (SWIG_TypeEquiv(iter->types[i]->str, name))) + return iter->types[i]; + } + iter = iter->next; + } while (iter != end); + } + + /* neither found a match */ + return 0; +} + +/* + Pack binary data into a string +*/ +SWIGRUNTIME char * +SWIG_PackData(char *c, void *ptr, size_t sz) { + static const char hex[17] = "0123456789abcdef"; + const unsigned char *u = (unsigned char *) ptr; + const unsigned char *eu = u + sz; + for (; u != eu; ++u) { + unsigned char uu = *u; + *(c++) = hex[(uu & 0xf0) >> 4]; + *(c++) = hex[uu & 0xf]; + } + return c; +} + +/* + Unpack binary data from a string +*/ +SWIGRUNTIME const char * +SWIG_UnpackData(const char *c, void *ptr, size_t sz) { + unsigned char *u = (unsigned char *) ptr; + const unsigned char *eu = u + sz; + for (; u != eu; ++u) { + char d = *(c++); + unsigned char uu; + if ((d >= '0') && (d <= '9')) + uu = ((d - '0') << 4); + else if ((d >= 'a') && (d <= 'f')) + uu = ((d - ('a'-10)) << 4); + else + return (char *) 0; + d = *(c++); + if ((d >= '0') && (d <= '9')) + uu |= (d - '0'); + else if ((d >= 'a') && (d <= 'f')) + uu |= (d - ('a'-10)); + else + return (char *) 0; + *u = uu; + } + return c; +} + +/* + Pack 'void *' into a string buffer. +*/ +SWIGRUNTIME char * +SWIG_PackVoidPtr(char *buff, void *ptr, const char *name, size_t bsz) { + char *r = buff; + if ((2*sizeof(void *) + 2) > bsz) return 0; + *(r++) = '_'; + r = SWIG_PackData(r,&ptr,sizeof(void *)); + if (strlen(name) + 1 > (bsz - (r - buff))) return 0; + strcpy(r,name); + return buff; +} + +SWIGRUNTIME const char * +SWIG_UnpackVoidPtr(const char *c, void **ptr, const char *name) { + if (*c != '_') { + if (strcmp(c,"NULL") == 0) { + *ptr = (void *) 0; + return name; + } else { + return 0; + } + } + return SWIG_UnpackData(++c,ptr,sizeof(void *)); +} + +SWIGRUNTIME char * +SWIG_PackDataName(char *buff, void *ptr, size_t sz, const char *name, size_t bsz) { + char *r = buff; + size_t lname = (name ? strlen(name) : 0); + if ((2*sz + 2 + lname) > bsz) return 0; + *(r++) = '_'; + r = SWIG_PackData(r,ptr,sz); + if (lname) { + strncpy(r,name,lname+1); + } else { + *r = 0; + } + return buff; +} + +SWIGRUNTIME const char * +SWIG_UnpackDataName(const char *c, void *ptr, size_t sz, const char *name) { + if (*c != '_') { + if (strcmp(c,"NULL") == 0) { + memset(ptr,0,sz); + return name; + } else { + return 0; + } + } + return SWIG_UnpackData(++c,ptr,sz); +} + +#ifdef __cplusplus +} +#endif + +/* Errors in SWIG */ +#define SWIG_UnknownError -1 +#define SWIG_IOError -2 +#define SWIG_RuntimeError -3 +#define SWIG_IndexError -4 +#define SWIG_TypeError -5 +#define SWIG_DivisionByZero -6 +#define SWIG_OverflowError -7 +#define SWIG_SyntaxError -8 +#define SWIG_ValueError -9 +#define SWIG_SystemError -10 +#define SWIG_AttributeError -11 +#define SWIG_MemoryError -12 +#define SWIG_NullReferenceError -13 + + + +/* Compatibility macros for Python 3 */ +#if PY_VERSION_HEX >= 0x03000000 + +#define PyClass_Check(obj) PyObject_IsInstance(obj, (PyObject *)&PyType_Type) +#define PyInt_Check(x) PyLong_Check(x) +#define PyInt_AsLong(x) PyLong_AsLong(x) +#define PyInt_FromLong(x) PyLong_FromLong(x) +#define PyInt_FromSize_t(x) PyLong_FromSize_t(x) +#define PyString_Check(name) PyBytes_Check(name) +#define PyString_FromString(x) PyUnicode_FromString(x) +#define PyString_Format(fmt, args) PyUnicode_Format(fmt, args) +#define PyString_AsString(str) PyBytes_AsString(str) +#define PyString_Size(str) PyBytes_Size(str) +#define PyString_InternFromString(key) PyUnicode_InternFromString(key) +#define Py_TPFLAGS_HAVE_CLASS Py_TPFLAGS_BASETYPE +#define PyString_AS_STRING(x) PyUnicode_AS_STRING(x) +#define _PyLong_FromSsize_t(x) PyLong_FromSsize_t(x) + +#endif + +#ifndef Py_TYPE +# define Py_TYPE(op) ((op)->ob_type) +#endif + +/* SWIG APIs for compatibility of both Python 2 & 3 */ + +#if PY_VERSION_HEX >= 0x03000000 +# define SWIG_Python_str_FromFormat PyUnicode_FromFormat +#else +# define SWIG_Python_str_FromFormat PyString_FromFormat +#endif + + +/* Warning: This function will allocate a new string in Python 3, + * so please call SWIG_Python_str_DelForPy3(x) to free the space. + */ +SWIGINTERN char* +SWIG_Python_str_AsChar(PyObject *str) +{ +#if PY_VERSION_HEX >= 0x03000000 + char *cstr; + char *newstr; + Py_ssize_t len; + str = PyUnicode_AsUTF8String(str); + PyBytes_AsStringAndSize(str, &cstr, &len); + newstr = (char *) malloc(len+1); + memcpy(newstr, cstr, len+1); + Py_XDECREF(str); + return newstr; +#else + return PyString_AsString(str); +#endif +} + +#if PY_VERSION_HEX >= 0x03000000 +# define SWIG_Python_str_DelForPy3(x) free( (void*) (x) ) +#else +# define SWIG_Python_str_DelForPy3(x) +#endif + + +SWIGINTERN PyObject* +SWIG_Python_str_FromChar(const char *c) +{ +#if PY_VERSION_HEX >= 0x03000000 + return PyUnicode_FromString(c); +#else + return PyString_FromString(c); +#endif +} + +/* Add PyOS_snprintf for old Pythons */ +#if PY_VERSION_HEX < 0x02020000 +# if defined(_MSC_VER) || defined(__BORLANDC__) || defined(_WATCOM) +# define PyOS_snprintf _snprintf +# else +# define PyOS_snprintf snprintf +# endif +#endif + +/* A crude PyString_FromFormat implementation for old Pythons */ +#if PY_VERSION_HEX < 0x02020000 + +#ifndef SWIG_PYBUFFER_SIZE +# define SWIG_PYBUFFER_SIZE 1024 +#endif + +static PyObject * +PyString_FromFormat(const char *fmt, ...) { + va_list ap; + char buf[SWIG_PYBUFFER_SIZE * 2]; + int res; + va_start(ap, fmt); + res = vsnprintf(buf, sizeof(buf), fmt, ap); + va_end(ap); + return (res < 0 || res >= (int)sizeof(buf)) ? 0 : PyString_FromString(buf); +} +#endif + +/* Add PyObject_Del for old Pythons */ +#if PY_VERSION_HEX < 0x01060000 +# define PyObject_Del(op) PyMem_DEL((op)) +#endif +#ifndef PyObject_DEL +# define PyObject_DEL PyObject_Del +#endif + +/* A crude PyExc_StopIteration exception for old Pythons */ +#if PY_VERSION_HEX < 0x02020000 +# ifndef PyExc_StopIteration +# define PyExc_StopIteration PyExc_RuntimeError +# endif +# ifndef PyObject_GenericGetAttr +# define PyObject_GenericGetAttr 0 +# endif +#endif + +/* Py_NotImplemented is defined in 2.1 and up. */ +#if PY_VERSION_HEX < 0x02010000 +# ifndef Py_NotImplemented +# define Py_NotImplemented PyExc_RuntimeError +# endif +#endif + +/* A crude PyString_AsStringAndSize implementation for old Pythons */ +#if PY_VERSION_HEX < 0x02010000 +# ifndef PyString_AsStringAndSize +# define PyString_AsStringAndSize(obj, s, len) {*s = PyString_AsString(obj); *len = *s ? strlen(*s) : 0;} +# endif +#endif + +/* PySequence_Size for old Pythons */ +#if PY_VERSION_HEX < 0x02000000 +# ifndef PySequence_Size +# define PySequence_Size PySequence_Length +# endif +#endif + +/* PyBool_FromLong for old Pythons */ +#if PY_VERSION_HEX < 0x02030000 +static +PyObject *PyBool_FromLong(long ok) +{ + PyObject *result = ok ? Py_True : Py_False; + Py_INCREF(result); + return result; +} +#endif + +/* Py_ssize_t for old Pythons */ +/* This code is as recommended by: */ +/* http://www.python.org/dev/peps/pep-0353/#conversion-guidelines */ +#if PY_VERSION_HEX < 0x02050000 && !defined(PY_SSIZE_T_MIN) +typedef int Py_ssize_t; +# define PY_SSIZE_T_MAX INT_MAX +# define PY_SSIZE_T_MIN INT_MIN +typedef inquiry lenfunc; +typedef intargfunc ssizeargfunc; +typedef intintargfunc ssizessizeargfunc; +typedef intobjargproc ssizeobjargproc; +typedef intintobjargproc ssizessizeobjargproc; +typedef getreadbufferproc readbufferproc; +typedef getwritebufferproc writebufferproc; +typedef getsegcountproc segcountproc; +typedef getcharbufferproc charbufferproc; +static long PyNumber_AsSsize_t (PyObject *x, void *SWIGUNUSEDPARM(exc)) +{ + long result = 0; + PyObject *i = PyNumber_Int(x); + if (i) { + result = PyInt_AsLong(i); + Py_DECREF(i); + } + return result; +} +#endif + +#if PY_VERSION_HEX < 0x02050000 +#define PyInt_FromSize_t(x) PyInt_FromLong((long)x) +#endif + +#if PY_VERSION_HEX < 0x02040000 +#define Py_VISIT(op) \ + do { \ + if (op) { \ + int vret = visit((op), arg); \ + if (vret) \ + return vret; \ + } \ + } while (0) +#endif + +#if PY_VERSION_HEX < 0x02030000 +typedef struct { + PyTypeObject type; + PyNumberMethods as_number; + PyMappingMethods as_mapping; + PySequenceMethods as_sequence; + PyBufferProcs as_buffer; + PyObject *name, *slots; +} PyHeapTypeObject; +#endif + +#if PY_VERSION_HEX < 0x02030000 +typedef destructor freefunc; +#endif + +#if ((PY_MAJOR_VERSION == 2 && PY_MINOR_VERSION > 6) || \ + (PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION > 0) || \ + (PY_MAJOR_VERSION > 3)) +# define SWIGPY_USE_CAPSULE +# define SWIGPY_CAPSULE_NAME ((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION ".type_pointer_capsule" SWIG_TYPE_TABLE_NAME) +#endif + +#if PY_VERSION_HEX < 0x03020000 +#define PyDescr_TYPE(x) (((PyDescrObject *)(x))->d_type) +#define PyDescr_NAME(x) (((PyDescrObject *)(x))->d_name) +#endif + +/* ----------------------------------------------------------------------------- + * error manipulation + * ----------------------------------------------------------------------------- */ + +SWIGRUNTIME PyObject* +SWIG_Python_ErrorType(int code) { + PyObject* type = 0; + switch(code) { + case SWIG_MemoryError: + type = PyExc_MemoryError; + break; + case SWIG_IOError: + type = PyExc_IOError; + break; + case SWIG_RuntimeError: + type = PyExc_RuntimeError; + break; + case SWIG_IndexError: + type = PyExc_IndexError; + break; + case SWIG_TypeError: + type = PyExc_TypeError; + break; + case SWIG_DivisionByZero: + type = PyExc_ZeroDivisionError; + break; + case SWIG_OverflowError: + type = PyExc_OverflowError; + break; + case SWIG_SyntaxError: + type = PyExc_SyntaxError; + break; + case SWIG_ValueError: + type = PyExc_ValueError; + break; + case SWIG_SystemError: + type = PyExc_SystemError; + break; + case SWIG_AttributeError: + type = PyExc_AttributeError; + break; + default: + type = PyExc_RuntimeError; + } + return type; +} + + +SWIGRUNTIME void +SWIG_Python_AddErrorMsg(const char* mesg) +{ + PyObject *type = 0; + PyObject *value = 0; + PyObject *traceback = 0; + + if (PyErr_Occurred()) PyErr_Fetch(&type, &value, &traceback); + if (value) { + char *tmp; + PyObject *old_str = PyObject_Str(value); + PyErr_Clear(); + Py_XINCREF(type); + + PyErr_Format(type, "%s %s", tmp = SWIG_Python_str_AsChar(old_str), mesg); + SWIG_Python_str_DelForPy3(tmp); + Py_DECREF(old_str); + Py_DECREF(value); + } else { + PyErr_SetString(PyExc_RuntimeError, mesg); + } +} + +#if defined(SWIG_PYTHON_NO_THREADS) +# if defined(SWIG_PYTHON_THREADS) +# undef SWIG_PYTHON_THREADS +# endif +#endif +#if defined(SWIG_PYTHON_THREADS) /* Threading support is enabled */ +# if !defined(SWIG_PYTHON_USE_GIL) && !defined(SWIG_PYTHON_NO_USE_GIL) +# if (PY_VERSION_HEX >= 0x02030000) /* For 2.3 or later, use the PyGILState calls */ +# define SWIG_PYTHON_USE_GIL +# endif +# endif +# if defined(SWIG_PYTHON_USE_GIL) /* Use PyGILState threads calls */ +# ifndef SWIG_PYTHON_INITIALIZE_THREADS +# define SWIG_PYTHON_INITIALIZE_THREADS PyEval_InitThreads() +# endif +# ifdef __cplusplus /* C++ code */ + class SWIG_Python_Thread_Block { + bool status; + PyGILState_STATE state; + public: + void end() { if (status) { PyGILState_Release(state); status = false;} } + SWIG_Python_Thread_Block() : status(true), state(PyGILState_Ensure()) {} + ~SWIG_Python_Thread_Block() { end(); } + }; + class SWIG_Python_Thread_Allow { + bool status; + PyThreadState *save; + public: + void end() { if (status) { PyEval_RestoreThread(save); status = false; }} + SWIG_Python_Thread_Allow() : status(true), save(PyEval_SaveThread()) {} + ~SWIG_Python_Thread_Allow() { end(); } + }; +# define SWIG_PYTHON_THREAD_BEGIN_BLOCK SWIG_Python_Thread_Block _swig_thread_block +# define SWIG_PYTHON_THREAD_END_BLOCK _swig_thread_block.end() +# define SWIG_PYTHON_THREAD_BEGIN_ALLOW SWIG_Python_Thread_Allow _swig_thread_allow +# define SWIG_PYTHON_THREAD_END_ALLOW _swig_thread_allow.end() +# else /* C code */ +# define SWIG_PYTHON_THREAD_BEGIN_BLOCK PyGILState_STATE _swig_thread_block = PyGILState_Ensure() +# define SWIG_PYTHON_THREAD_END_BLOCK PyGILState_Release(_swig_thread_block) +# define SWIG_PYTHON_THREAD_BEGIN_ALLOW PyThreadState *_swig_thread_allow = PyEval_SaveThread() +# define SWIG_PYTHON_THREAD_END_ALLOW PyEval_RestoreThread(_swig_thread_allow) +# endif +# else /* Old thread way, not implemented, user must provide it */ +# if !defined(SWIG_PYTHON_INITIALIZE_THREADS) +# define SWIG_PYTHON_INITIALIZE_THREADS +# endif +# if !defined(SWIG_PYTHON_THREAD_BEGIN_BLOCK) +# define SWIG_PYTHON_THREAD_BEGIN_BLOCK +# endif +# if !defined(SWIG_PYTHON_THREAD_END_BLOCK) +# define SWIG_PYTHON_THREAD_END_BLOCK +# endif +# if !defined(SWIG_PYTHON_THREAD_BEGIN_ALLOW) +# define SWIG_PYTHON_THREAD_BEGIN_ALLOW +# endif +# if !defined(SWIG_PYTHON_THREAD_END_ALLOW) +# define SWIG_PYTHON_THREAD_END_ALLOW +# endif +# endif +#else /* No thread support */ +# define SWIG_PYTHON_INITIALIZE_THREADS +# define SWIG_PYTHON_THREAD_BEGIN_BLOCK +# define SWIG_PYTHON_THREAD_END_BLOCK +# define SWIG_PYTHON_THREAD_BEGIN_ALLOW +# define SWIG_PYTHON_THREAD_END_ALLOW +#endif + +/* ----------------------------------------------------------------------------- + * Python API portion that goes into the runtime + * ----------------------------------------------------------------------------- */ + +#ifdef __cplusplus +extern "C" { +#endif + +/* ----------------------------------------------------------------------------- + * Constant declarations + * ----------------------------------------------------------------------------- */ + +/* Constant Types */ +#define SWIG_PY_POINTER 4 +#define SWIG_PY_BINARY 5 + +/* Constant information structure */ +typedef struct swig_const_info { + int type; + char *name; + long lvalue; + double dvalue; + void *pvalue; + swig_type_info **ptype; +} swig_const_info; + + +/* ----------------------------------------------------------------------------- + * Wrapper of PyInstanceMethod_New() used in Python 3 + * It is exported to the generated module, used for -fastproxy + * ----------------------------------------------------------------------------- */ +#if PY_VERSION_HEX >= 0x03000000 +SWIGRUNTIME PyObject* SWIG_PyInstanceMethod_New(PyObject *SWIGUNUSEDPARM(self), PyObject *func) +{ + return PyInstanceMethod_New(func); +} +#else +SWIGRUNTIME PyObject* SWIG_PyInstanceMethod_New(PyObject *SWIGUNUSEDPARM(self), PyObject *SWIGUNUSEDPARM(func)) +{ + return NULL; +} +#endif + +#ifdef __cplusplus +} +#endif + + +/* ----------------------------------------------------------------------------- + * pyrun.swg + * + * This file contains the runtime support for Python modules + * and includes code for managing global variables and pointer + * type checking. + * + * ----------------------------------------------------------------------------- */ + +/* Common SWIG API */ + +/* for raw pointers */ +#define SWIG_Python_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, 0) +#define SWIG_ConvertPtr(obj, pptr, type, flags) SWIG_Python_ConvertPtr(obj, pptr, type, flags) +#define SWIG_ConvertPtrAndOwn(obj,pptr,type,flags,own) SWIG_Python_ConvertPtrAndOwn(obj, pptr, type, flags, own) + +#ifdef SWIGPYTHON_BUILTIN +#define SWIG_NewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(self, ptr, type, flags) +#else +#define SWIG_NewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(NULL, ptr, type, flags) +#endif + +#define SWIG_InternalNewPointerObj(ptr, type, flags) SWIG_Python_NewPointerObj(NULL, ptr, type, flags) + +#define SWIG_CheckImplicit(ty) SWIG_Python_CheckImplicit(ty) +#define SWIG_AcquirePtr(ptr, src) SWIG_Python_AcquirePtr(ptr, src) +#define swig_owntype int + +/* for raw packed data */ +#define SWIG_ConvertPacked(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) +#define SWIG_NewPackedObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) + +/* for class or struct pointers */ +#define SWIG_ConvertInstance(obj, pptr, type, flags) SWIG_ConvertPtr(obj, pptr, type, flags) +#define SWIG_NewInstanceObj(ptr, type, flags) SWIG_NewPointerObj(ptr, type, flags) + +/* for C or C++ function pointers */ +#define SWIG_ConvertFunctionPtr(obj, pptr, type) SWIG_Python_ConvertFunctionPtr(obj, pptr, type) +#define SWIG_NewFunctionPtrObj(ptr, type) SWIG_Python_NewPointerObj(NULL, ptr, type, 0) + +/* for C++ member pointers, ie, member methods */ +#define SWIG_ConvertMember(obj, ptr, sz, ty) SWIG_Python_ConvertPacked(obj, ptr, sz, ty) +#define SWIG_NewMemberObj(ptr, sz, type) SWIG_Python_NewPackedObj(ptr, sz, type) + + +/* Runtime API */ + +#define SWIG_GetModule(clientdata) SWIG_Python_GetModule(clientdata) +#define SWIG_SetModule(clientdata, pointer) SWIG_Python_SetModule(pointer) +#define SWIG_NewClientData(obj) SwigPyClientData_New(obj) + +#define SWIG_SetErrorObj SWIG_Python_SetErrorObj +#define SWIG_SetErrorMsg SWIG_Python_SetErrorMsg +#define SWIG_ErrorType(code) SWIG_Python_ErrorType(code) +#define SWIG_Error(code, msg) SWIG_Python_SetErrorMsg(SWIG_ErrorType(code), msg) +#define SWIG_fail goto fail + + +/* Runtime API implementation */ + +/* Error manipulation */ + +SWIGINTERN void +SWIG_Python_SetErrorObj(PyObject *errtype, PyObject *obj) { + SWIG_PYTHON_THREAD_BEGIN_BLOCK; + PyErr_SetObject(errtype, obj); + Py_DECREF(obj); + SWIG_PYTHON_THREAD_END_BLOCK; +} + +SWIGINTERN void +SWIG_Python_SetErrorMsg(PyObject *errtype, const char *msg) { + SWIG_PYTHON_THREAD_BEGIN_BLOCK; + PyErr_SetString(errtype, msg); + SWIG_PYTHON_THREAD_END_BLOCK; +} + +#define SWIG_Python_Raise(obj, type, desc) SWIG_Python_SetErrorObj(SWIG_Python_ExceptionType(desc), obj) + +/* Set a constant value */ + +#if defined(SWIGPYTHON_BUILTIN) + +SWIGINTERN void +SwigPyBuiltin_AddPublicSymbol(PyObject *seq, const char *key) { + PyObject *s = PyString_InternFromString(key); + PyList_Append(seq, s); + Py_DECREF(s); +} + +SWIGINTERN void +SWIG_Python_SetConstant(PyObject *d, PyObject *public_interface, const char *name, PyObject *obj) { +#if PY_VERSION_HEX < 0x02030000 + PyDict_SetItemString(d, (char *)name, obj); +#else + PyDict_SetItemString(d, name, obj); +#endif + Py_DECREF(obj); + if (public_interface) + SwigPyBuiltin_AddPublicSymbol(public_interface, name); +} + +#else + +SWIGINTERN void +SWIG_Python_SetConstant(PyObject *d, const char *name, PyObject *obj) { +#if PY_VERSION_HEX < 0x02030000 + PyDict_SetItemString(d, (char *)name, obj); +#else + PyDict_SetItemString(d, name, obj); +#endif + Py_DECREF(obj); +} + +#endif + +/* Append a value to the result obj */ + +SWIGINTERN PyObject* +SWIG_Python_AppendOutput(PyObject* result, PyObject* obj) { +#if !defined(SWIG_PYTHON_OUTPUT_TUPLE) + if (!result) { + result = obj; + } else if (result == Py_None) { + Py_DECREF(result); + result = obj; + } else { + if (!PyList_Check(result)) { + PyObject *o2 = result; + result = PyList_New(1); + PyList_SetItem(result, 0, o2); + } + PyList_Append(result,obj); + Py_DECREF(obj); + } + return result; +#else + PyObject* o2; + PyObject* o3; + if (!result) { + result = obj; + } else if (result == Py_None) { + Py_DECREF(result); + result = obj; + } else { + if (!PyTuple_Check(result)) { + o2 = result; + result = PyTuple_New(1); + PyTuple_SET_ITEM(result, 0, o2); + } + o3 = PyTuple_New(1); + PyTuple_SET_ITEM(o3, 0, obj); + o2 = result; + result = PySequence_Concat(o2, o3); + Py_DECREF(o2); + Py_DECREF(o3); + } + return result; +#endif +} + +/* Unpack the argument tuple */ + +SWIGINTERN Py_ssize_t +SWIG_Python_UnpackTuple(PyObject *args, const char *name, Py_ssize_t min, Py_ssize_t max, PyObject **objs) +{ + if (!args) { + if (!min && !max) { + return 1; + } else { + PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got none", + name, (min == max ? "" : "at least "), (int)min); + return 0; + } + } + if (!PyTuple_Check(args)) { + if (min <= 1 && max >= 1) { + Py_ssize_t i; + objs[0] = args; + for (i = 1; i < max; ++i) { + objs[i] = 0; + } + return 2; + } + PyErr_SetString(PyExc_SystemError, "UnpackTuple() argument list is not a tuple"); + return 0; + } else { + Py_ssize_t l = PyTuple_GET_SIZE(args); + if (l < min) { + PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", + name, (min == max ? "" : "at least "), (int)min, (int)l); + return 0; + } else if (l > max) { + PyErr_Format(PyExc_TypeError, "%s expected %s%d arguments, got %d", + name, (min == max ? "" : "at most "), (int)max, (int)l); + return 0; + } else { + Py_ssize_t i; + for (i = 0; i < l; ++i) { + objs[i] = PyTuple_GET_ITEM(args, i); + } + for (; l < max; ++l) { + objs[l] = 0; + } + return i + 1; + } + } +} + +/* A functor is a function object with one single object argument */ +#if PY_VERSION_HEX >= 0x02020000 +#define SWIG_Python_CallFunctor(functor, obj) PyObject_CallFunctionObjArgs(functor, obj, NULL); +#else +#define SWIG_Python_CallFunctor(functor, obj) PyObject_CallFunction(functor, "O", obj); +#endif + +/* + Helper for static pointer initialization for both C and C++ code, for example + static PyObject *SWIG_STATIC_POINTER(MyVar) = NewSomething(...); +*/ +#ifdef __cplusplus +#define SWIG_STATIC_POINTER(var) var +#else +#define SWIG_STATIC_POINTER(var) var = 0; if (!var) var +#endif + +/* ----------------------------------------------------------------------------- + * Pointer declarations + * ----------------------------------------------------------------------------- */ + +/* Flags for new pointer objects */ +#define SWIG_POINTER_NOSHADOW (SWIG_POINTER_OWN << 1) +#define SWIG_POINTER_NEW (SWIG_POINTER_NOSHADOW | SWIG_POINTER_OWN) + +#define SWIG_POINTER_IMPLICIT_CONV (SWIG_POINTER_DISOWN << 1) + +#define SWIG_BUILTIN_TP_INIT (SWIG_POINTER_OWN << 2) +#define SWIG_BUILTIN_INIT (SWIG_BUILTIN_TP_INIT | SWIG_POINTER_OWN) + +#ifdef __cplusplus +extern "C" { +#endif + +/* How to access Py_None */ +#if defined(_WIN32) || defined(__WIN32__) || defined(__CYGWIN__) +# ifndef SWIG_PYTHON_NO_BUILD_NONE +# ifndef SWIG_PYTHON_BUILD_NONE +# define SWIG_PYTHON_BUILD_NONE +# endif +# endif +#endif + +#ifdef SWIG_PYTHON_BUILD_NONE +# ifdef Py_None +# undef Py_None +# define Py_None SWIG_Py_None() +# endif +SWIGRUNTIMEINLINE PyObject * +_SWIG_Py_None(void) +{ + PyObject *none = Py_BuildValue((char*)""); + Py_DECREF(none); + return none; +} +SWIGRUNTIME PyObject * +SWIG_Py_None(void) +{ + static PyObject *SWIG_STATIC_POINTER(none) = _SWIG_Py_None(); + return none; +} +#endif + +/* The python void return value */ + +SWIGRUNTIMEINLINE PyObject * +SWIG_Py_Void(void) +{ + PyObject *none = Py_None; + Py_INCREF(none); + return none; +} + +/* SwigPyClientData */ + +typedef struct { + PyObject *klass; + PyObject *newraw; + PyObject *newargs; + PyObject *destroy; + int delargs; + int implicitconv; + PyTypeObject *pytype; +} SwigPyClientData; + +SWIGRUNTIMEINLINE int +SWIG_Python_CheckImplicit(swig_type_info *ty) +{ + SwigPyClientData *data = (SwigPyClientData *)ty->clientdata; + return data ? data->implicitconv : 0; +} + +SWIGRUNTIMEINLINE PyObject * +SWIG_Python_ExceptionType(swig_type_info *desc) { + SwigPyClientData *data = desc ? (SwigPyClientData *) desc->clientdata : 0; + PyObject *klass = data ? data->klass : 0; + return (klass ? klass : PyExc_RuntimeError); +} + + +SWIGRUNTIME SwigPyClientData * +SwigPyClientData_New(PyObject* obj) +{ + if (!obj) { + return 0; + } else { + SwigPyClientData *data = (SwigPyClientData *)malloc(sizeof(SwigPyClientData)); + /* the klass element */ + data->klass = obj; + Py_INCREF(data->klass); + /* the newraw method and newargs arguments used to create a new raw instance */ + if (PyClass_Check(obj)) { + data->newraw = 0; + data->newargs = obj; + Py_INCREF(obj); + } else { +#if (PY_VERSION_HEX < 0x02020000) + data->newraw = 0; +#else + data->newraw = PyObject_GetAttrString(data->klass, (char *)"__new__"); +#endif + if (data->newraw) { + Py_INCREF(data->newraw); + data->newargs = PyTuple_New(1); + PyTuple_SetItem(data->newargs, 0, obj); + } else { + data->newargs = obj; + } + Py_INCREF(data->newargs); + } + /* the destroy method, aka as the C++ delete method */ + data->destroy = PyObject_GetAttrString(data->klass, (char *)"__swig_destroy__"); + if (PyErr_Occurred()) { + PyErr_Clear(); + data->destroy = 0; + } + if (data->destroy) { + int flags; + Py_INCREF(data->destroy); + flags = PyCFunction_GET_FLAGS(data->destroy); +#ifdef METH_O + data->delargs = !(flags & (METH_O)); +#else + data->delargs = 0; +#endif + } else { + data->delargs = 0; + } + data->implicitconv = 0; + data->pytype = 0; + return data; + } +} + +SWIGRUNTIME void +SwigPyClientData_Del(SwigPyClientData *data) { + Py_XDECREF(data->newraw); + Py_XDECREF(data->newargs); + Py_XDECREF(data->destroy); +} + +/* =============== SwigPyObject =====================*/ + +typedef struct { + PyObject_HEAD + void *ptr; + swig_type_info *ty; + int own; + PyObject *next; +#ifdef SWIGPYTHON_BUILTIN + PyObject *dict; +#endif +} SwigPyObject; + + +#ifdef SWIGPYTHON_BUILTIN + +SWIGRUNTIME PyObject * +SwigPyObject_get___dict__(PyObject *v, PyObject *SWIGUNUSEDPARM(args)) +{ + SwigPyObject *sobj = (SwigPyObject *)v; + + if (!sobj->dict) + sobj->dict = PyDict_New(); + + Py_INCREF(sobj->dict); + return sobj->dict; +} + +#endif + +SWIGRUNTIME PyObject * +SwigPyObject_long(SwigPyObject *v) +{ + return PyLong_FromVoidPtr(v->ptr); +} + +SWIGRUNTIME PyObject * +SwigPyObject_format(const char* fmt, SwigPyObject *v) +{ + PyObject *res = NULL; + PyObject *args = PyTuple_New(1); + if (args) { + if (PyTuple_SetItem(args, 0, SwigPyObject_long(v)) == 0) { + PyObject *ofmt = SWIG_Python_str_FromChar(fmt); + if (ofmt) { +#if PY_VERSION_HEX >= 0x03000000 + res = PyUnicode_Format(ofmt,args); +#else + res = PyString_Format(ofmt,args); +#endif + Py_DECREF(ofmt); + } + Py_DECREF(args); + } + } + return res; +} + +SWIGRUNTIME PyObject * +SwigPyObject_oct(SwigPyObject *v) +{ + return SwigPyObject_format("%o",v); +} + +SWIGRUNTIME PyObject * +SwigPyObject_hex(SwigPyObject *v) +{ + return SwigPyObject_format("%x",v); +} + +SWIGRUNTIME PyObject * +#ifdef METH_NOARGS +SwigPyObject_repr(SwigPyObject *v) +#else +SwigPyObject_repr(SwigPyObject *v, PyObject *args) +#endif +{ + const char *name = SWIG_TypePrettyName(v->ty); + PyObject *repr = SWIG_Python_str_FromFormat("", (name ? name : "unknown"), (void *)v); + if (v->next) { +# ifdef METH_NOARGS + PyObject *nrep = SwigPyObject_repr((SwigPyObject *)v->next); +# else + PyObject *nrep = SwigPyObject_repr((SwigPyObject *)v->next, args); +# endif +# if PY_VERSION_HEX >= 0x03000000 + PyObject *joined = PyUnicode_Concat(repr, nrep); + Py_DecRef(repr); + Py_DecRef(nrep); + repr = joined; +# else + PyString_ConcatAndDel(&repr,nrep); +# endif + } + return repr; +} + +SWIGRUNTIME int +SwigPyObject_compare(SwigPyObject *v, SwigPyObject *w) +{ + void *i = v->ptr; + void *j = w->ptr; + return (i < j) ? -1 : ((i > j) ? 1 : 0); +} + +/* Added for Python 3.x, would it also be useful for Python 2.x? */ +SWIGRUNTIME PyObject* +SwigPyObject_richcompare(SwigPyObject *v, SwigPyObject *w, int op) +{ + PyObject* res; + if( op != Py_EQ && op != Py_NE ) { + Py_INCREF(Py_NotImplemented); + return Py_NotImplemented; + } + res = PyBool_FromLong( (SwigPyObject_compare(v, w)==0) == (op == Py_EQ) ? 1 : 0); + return res; +} + + +SWIGRUNTIME PyTypeObject* SwigPyObject_TypeOnce(void); + +#ifdef SWIGPYTHON_BUILTIN +static swig_type_info *SwigPyObject_stype = 0; +SWIGRUNTIME PyTypeObject* +SwigPyObject_type(void) { + SwigPyClientData *cd; + assert(SwigPyObject_stype); + cd = (SwigPyClientData*) SwigPyObject_stype->clientdata; + assert(cd); + assert(cd->pytype); + return cd->pytype; +} +#else +SWIGRUNTIME PyTypeObject* +SwigPyObject_type(void) { + static PyTypeObject *SWIG_STATIC_POINTER(type) = SwigPyObject_TypeOnce(); + return type; +} +#endif + +SWIGRUNTIMEINLINE int +SwigPyObject_Check(PyObject *op) { +#ifdef SWIGPYTHON_BUILTIN + PyTypeObject *target_tp = SwigPyObject_type(); + if (PyType_IsSubtype(op->ob_type, target_tp)) + return 1; + return (strcmp(op->ob_type->tp_name, "SwigPyObject") == 0); +#else + return (Py_TYPE(op) == SwigPyObject_type()) + || (strcmp(Py_TYPE(op)->tp_name,"SwigPyObject") == 0); +#endif +} + +SWIGRUNTIME PyObject * +SwigPyObject_New(void *ptr, swig_type_info *ty, int own); + +SWIGRUNTIME void +SwigPyObject_dealloc(PyObject *v) +{ + SwigPyObject *sobj = (SwigPyObject *) v; + PyObject *next = sobj->next; + if (sobj->own == SWIG_POINTER_OWN) { + swig_type_info *ty = sobj->ty; + SwigPyClientData *data = ty ? (SwigPyClientData *) ty->clientdata : 0; + PyObject *destroy = data ? data->destroy : 0; + if (destroy) { + /* destroy is always a VARARGS method */ + PyObject *res; + + /* PyObject_CallFunction() has the potential to silently drop + the active active exception. In cases of unnamed temporary + variable or where we just finished iterating over a generator + StopIteration will be active right now, and this needs to + remain true upon return from SwigPyObject_dealloc. So save + and restore. */ + + PyObject *val = NULL, *type = NULL, *tb = NULL; + PyErr_Fetch(&val, &type, &tb); + + if (data->delargs) { + /* we need to create a temporary object to carry the destroy operation */ + PyObject *tmp = SwigPyObject_New(sobj->ptr, ty, 0); + res = SWIG_Python_CallFunctor(destroy, tmp); + Py_DECREF(tmp); + } else { + PyCFunction meth = PyCFunction_GET_FUNCTION(destroy); + PyObject *mself = PyCFunction_GET_SELF(destroy); + res = ((*meth)(mself, v)); + } + if (!res) + PyErr_WriteUnraisable(destroy); + + PyErr_Restore(val, type, tb); + + Py_XDECREF(res); + } +#if !defined(SWIG_PYTHON_SILENT_MEMLEAK) + else { + const char *name = SWIG_TypePrettyName(ty); + printf("swig/python detected a memory leak of type '%s', no destructor found.\n", (name ? name : "unknown")); + } +#endif + } + Py_XDECREF(next); + PyObject_DEL(v); +} + +SWIGRUNTIME PyObject* +SwigPyObject_append(PyObject* v, PyObject* next) +{ + SwigPyObject *sobj = (SwigPyObject *) v; +#ifndef METH_O + PyObject *tmp = 0; + if (!PyArg_ParseTuple(next,(char *)"O:append", &tmp)) return NULL; + next = tmp; +#endif + if (!SwigPyObject_Check(next)) { + PyErr_SetString(PyExc_TypeError, "Attempt to append a non SwigPyObject"); + return NULL; + } + sobj->next = next; + Py_INCREF(next); + return SWIG_Py_Void(); +} + +SWIGRUNTIME PyObject* +#ifdef METH_NOARGS +SwigPyObject_next(PyObject* v) +#else +SwigPyObject_next(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) +#endif +{ + SwigPyObject *sobj = (SwigPyObject *) v; + if (sobj->next) { + Py_INCREF(sobj->next); + return sobj->next; + } else { + return SWIG_Py_Void(); + } +} + +SWIGINTERN PyObject* +#ifdef METH_NOARGS +SwigPyObject_disown(PyObject *v) +#else +SwigPyObject_disown(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) +#endif +{ + SwigPyObject *sobj = (SwigPyObject *)v; + sobj->own = 0; + return SWIG_Py_Void(); +} + +SWIGINTERN PyObject* +#ifdef METH_NOARGS +SwigPyObject_acquire(PyObject *v) +#else +SwigPyObject_acquire(PyObject* v, PyObject *SWIGUNUSEDPARM(args)) +#endif +{ + SwigPyObject *sobj = (SwigPyObject *)v; + sobj->own = SWIG_POINTER_OWN; + return SWIG_Py_Void(); +} + +SWIGINTERN PyObject* +SwigPyObject_own(PyObject *v, PyObject *args) +{ + PyObject *val = 0; +#if (PY_VERSION_HEX < 0x02020000) + if (!PyArg_ParseTuple(args,(char *)"|O:own",&val)) +#elif (PY_VERSION_HEX < 0x02050000) + if (!PyArg_UnpackTuple(args, (char *)"own", 0, 1, &val)) +#else + if (!PyArg_UnpackTuple(args, "own", 0, 1, &val)) +#endif + { + return NULL; + } + else + { + SwigPyObject *sobj = (SwigPyObject *)v; + PyObject *obj = PyBool_FromLong(sobj->own); + if (val) { +#ifdef METH_NOARGS + if (PyObject_IsTrue(val)) { + SwigPyObject_acquire(v); + } else { + SwigPyObject_disown(v); + } +#else + if (PyObject_IsTrue(val)) { + SwigPyObject_acquire(v,args); + } else { + SwigPyObject_disown(v,args); + } +#endif + } + return obj; + } +} + +#ifdef METH_O +static PyMethodDef +swigobject_methods[] = { + {(char *)"disown", (PyCFunction)SwigPyObject_disown, METH_NOARGS, (char *)"releases ownership of the pointer"}, + {(char *)"acquire", (PyCFunction)SwigPyObject_acquire, METH_NOARGS, (char *)"acquires ownership of the pointer"}, + {(char *)"own", (PyCFunction)SwigPyObject_own, METH_VARARGS, (char *)"returns/sets ownership of the pointer"}, + {(char *)"append", (PyCFunction)SwigPyObject_append, METH_O, (char *)"appends another 'this' object"}, + {(char *)"next", (PyCFunction)SwigPyObject_next, METH_NOARGS, (char *)"returns the next 'this' object"}, + {(char *)"__repr__",(PyCFunction)SwigPyObject_repr, METH_NOARGS, (char *)"returns object representation"}, + {0, 0, 0, 0} +}; +#else +static PyMethodDef +swigobject_methods[] = { + {(char *)"disown", (PyCFunction)SwigPyObject_disown, METH_VARARGS, (char *)"releases ownership of the pointer"}, + {(char *)"acquire", (PyCFunction)SwigPyObject_acquire, METH_VARARGS, (char *)"acquires ownership of the pointer"}, + {(char *)"own", (PyCFunction)SwigPyObject_own, METH_VARARGS, (char *)"returns/sets ownership of the pointer"}, + {(char *)"append", (PyCFunction)SwigPyObject_append, METH_VARARGS, (char *)"appends another 'this' object"}, + {(char *)"next", (PyCFunction)SwigPyObject_next, METH_VARARGS, (char *)"returns the next 'this' object"}, + {(char *)"__repr__",(PyCFunction)SwigPyObject_repr, METH_VARARGS, (char *)"returns object representation"}, + {0, 0, 0, 0} +}; +#endif + +#if PY_VERSION_HEX < 0x02020000 +SWIGINTERN PyObject * +SwigPyObject_getattr(SwigPyObject *sobj,char *name) +{ + return Py_FindMethod(swigobject_methods, (PyObject *)sobj, name); +} +#endif + +SWIGRUNTIME PyTypeObject* +SwigPyObject_TypeOnce(void) { + static char swigobject_doc[] = "Swig object carries a C/C++ instance pointer"; + + static PyNumberMethods SwigPyObject_as_number = { + (binaryfunc)0, /*nb_add*/ + (binaryfunc)0, /*nb_subtract*/ + (binaryfunc)0, /*nb_multiply*/ + /* nb_divide removed in Python 3 */ +#if PY_VERSION_HEX < 0x03000000 + (binaryfunc)0, /*nb_divide*/ +#endif + (binaryfunc)0, /*nb_remainder*/ + (binaryfunc)0, /*nb_divmod*/ + (ternaryfunc)0,/*nb_power*/ + (unaryfunc)0, /*nb_negative*/ + (unaryfunc)0, /*nb_positive*/ + (unaryfunc)0, /*nb_absolute*/ + (inquiry)0, /*nb_nonzero*/ + 0, /*nb_invert*/ + 0, /*nb_lshift*/ + 0, /*nb_rshift*/ + 0, /*nb_and*/ + 0, /*nb_xor*/ + 0, /*nb_or*/ +#if PY_VERSION_HEX < 0x03000000 + 0, /*nb_coerce*/ +#endif + (unaryfunc)SwigPyObject_long, /*nb_int*/ +#if PY_VERSION_HEX < 0x03000000 + (unaryfunc)SwigPyObject_long, /*nb_long*/ +#else + 0, /*nb_reserved*/ +#endif + (unaryfunc)0, /*nb_float*/ +#if PY_VERSION_HEX < 0x03000000 + (unaryfunc)SwigPyObject_oct, /*nb_oct*/ + (unaryfunc)SwigPyObject_hex, /*nb_hex*/ +#endif +#if PY_VERSION_HEX >= 0x03050000 /* 3.5 */ + 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_inplace_matrix_multiply */ +#elif PY_VERSION_HEX >= 0x03000000 /* 3.0 */ + 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_index, nb_inplace_divide removed */ +#elif PY_VERSION_HEX >= 0x02050000 /* 2.5.0 */ + 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_index */ +#elif PY_VERSION_HEX >= 0x02020000 /* 2.2.0 */ + 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_inplace_true_divide */ +#elif PY_VERSION_HEX >= 0x02000000 /* 2.0.0 */ + 0,0,0,0,0,0,0,0,0,0,0 /* nb_inplace_add -> nb_inplace_or */ +#endif + }; + + static PyTypeObject swigpyobject_type; + static int type_init = 0; + if (!type_init) { + const PyTypeObject tmp = { + /* PyObject header changed in Python 3 */ +#if PY_VERSION_HEX >= 0x03000000 + PyVarObject_HEAD_INIT(NULL, 0) +#else + PyObject_HEAD_INIT(NULL) + 0, /* ob_size */ +#endif + (char *)"SwigPyObject", /* tp_name */ + sizeof(SwigPyObject), /* tp_basicsize */ + 0, /* tp_itemsize */ + (destructor)SwigPyObject_dealloc, /* tp_dealloc */ + 0, /* tp_print */ +#if PY_VERSION_HEX < 0x02020000 + (getattrfunc)SwigPyObject_getattr, /* tp_getattr */ +#else + (getattrfunc)0, /* tp_getattr */ +#endif + (setattrfunc)0, /* tp_setattr */ +#if PY_VERSION_HEX >= 0x03000000 + 0, /* tp_reserved in 3.0.1, tp_compare in 3.0.0 but not used */ +#else + (cmpfunc)SwigPyObject_compare, /* tp_compare */ +#endif + (reprfunc)SwigPyObject_repr, /* tp_repr */ + &SwigPyObject_as_number, /* tp_as_number */ + 0, /* tp_as_sequence */ + 0, /* tp_as_mapping */ + (hashfunc)0, /* tp_hash */ + (ternaryfunc)0, /* tp_call */ + 0, /* tp_str */ + PyObject_GenericGetAttr, /* tp_getattro */ + 0, /* tp_setattro */ + 0, /* tp_as_buffer */ + Py_TPFLAGS_DEFAULT, /* tp_flags */ + swigobject_doc, /* tp_doc */ + 0, /* tp_traverse */ + 0, /* tp_clear */ + (richcmpfunc)SwigPyObject_richcompare,/* tp_richcompare */ + 0, /* tp_weaklistoffset */ +#if PY_VERSION_HEX >= 0x02020000 + 0, /* tp_iter */ + 0, /* tp_iternext */ + swigobject_methods, /* tp_methods */ + 0, /* tp_members */ + 0, /* tp_getset */ + 0, /* tp_base */ + 0, /* tp_dict */ + 0, /* tp_descr_get */ + 0, /* tp_descr_set */ + 0, /* tp_dictoffset */ + 0, /* tp_init */ + 0, /* tp_alloc */ + 0, /* tp_new */ + 0, /* tp_free */ + 0, /* tp_is_gc */ + 0, /* tp_bases */ + 0, /* tp_mro */ + 0, /* tp_cache */ + 0, /* tp_subclasses */ + 0, /* tp_weaklist */ +#endif +#if PY_VERSION_HEX >= 0x02030000 + 0, /* tp_del */ +#endif +#if PY_VERSION_HEX >= 0x02060000 + 0, /* tp_version_tag */ +#endif +#if PY_VERSION_HEX >= 0x03040000 + 0, /* tp_finalize */ +#endif +#ifdef COUNT_ALLOCS + 0, /* tp_allocs */ + 0, /* tp_frees */ + 0, /* tp_maxalloc */ +#if PY_VERSION_HEX >= 0x02050000 + 0, /* tp_prev */ +#endif + 0 /* tp_next */ +#endif + }; + swigpyobject_type = tmp; + type_init = 1; +#if PY_VERSION_HEX < 0x02020000 + swigpyobject_type.ob_type = &PyType_Type; +#else + if (PyType_Ready(&swigpyobject_type) < 0) + return NULL; +#endif + } + return &swigpyobject_type; +} + +SWIGRUNTIME PyObject * +SwigPyObject_New(void *ptr, swig_type_info *ty, int own) +{ + SwigPyObject *sobj = PyObject_NEW(SwigPyObject, SwigPyObject_type()); + if (sobj) { + sobj->ptr = ptr; + sobj->ty = ty; + sobj->own = own; + sobj->next = 0; + } + return (PyObject *)sobj; +} + +/* ----------------------------------------------------------------------------- + * Implements a simple Swig Packed type, and use it instead of string + * ----------------------------------------------------------------------------- */ + +typedef struct { + PyObject_HEAD + void *pack; + swig_type_info *ty; + size_t size; +} SwigPyPacked; + +SWIGRUNTIME int +SwigPyPacked_print(SwigPyPacked *v, FILE *fp, int SWIGUNUSEDPARM(flags)) +{ + char result[SWIG_BUFFER_SIZE]; + fputs("pack, v->size, 0, sizeof(result))) { + fputs("at ", fp); + fputs(result, fp); + } + fputs(v->ty->name,fp); + fputs(">", fp); + return 0; +} + +SWIGRUNTIME PyObject * +SwigPyPacked_repr(SwigPyPacked *v) +{ + char result[SWIG_BUFFER_SIZE]; + if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))) { + return SWIG_Python_str_FromFormat("", result, v->ty->name); + } else { + return SWIG_Python_str_FromFormat("", v->ty->name); + } +} + +SWIGRUNTIME PyObject * +SwigPyPacked_str(SwigPyPacked *v) +{ + char result[SWIG_BUFFER_SIZE]; + if (SWIG_PackDataName(result, v->pack, v->size, 0, sizeof(result))){ + return SWIG_Python_str_FromFormat("%s%s", result, v->ty->name); + } else { + return SWIG_Python_str_FromChar(v->ty->name); + } +} + +SWIGRUNTIME int +SwigPyPacked_compare(SwigPyPacked *v, SwigPyPacked *w) +{ + size_t i = v->size; + size_t j = w->size; + int s = (i < j) ? -1 : ((i > j) ? 1 : 0); + return s ? s : strncmp((char *)v->pack, (char *)w->pack, 2*v->size); +} + +SWIGRUNTIME PyTypeObject* SwigPyPacked_TypeOnce(void); + +SWIGRUNTIME PyTypeObject* +SwigPyPacked_type(void) { + static PyTypeObject *SWIG_STATIC_POINTER(type) = SwigPyPacked_TypeOnce(); + return type; +} + +SWIGRUNTIMEINLINE int +SwigPyPacked_Check(PyObject *op) { + return ((op)->ob_type == SwigPyPacked_TypeOnce()) + || (strcmp((op)->ob_type->tp_name,"SwigPyPacked") == 0); +} + +SWIGRUNTIME void +SwigPyPacked_dealloc(PyObject *v) +{ + if (SwigPyPacked_Check(v)) { + SwigPyPacked *sobj = (SwigPyPacked *) v; + free(sobj->pack); + } + PyObject_DEL(v); +} + +SWIGRUNTIME PyTypeObject* +SwigPyPacked_TypeOnce(void) { + static char swigpacked_doc[] = "Swig object carries a C/C++ instance pointer"; + static PyTypeObject swigpypacked_type; + static int type_init = 0; + if (!type_init) { + const PyTypeObject tmp = { + /* PyObject header changed in Python 3 */ +#if PY_VERSION_HEX>=0x03000000 + PyVarObject_HEAD_INIT(NULL, 0) +#else + PyObject_HEAD_INIT(NULL) + 0, /* ob_size */ +#endif + (char *)"SwigPyPacked", /* tp_name */ + sizeof(SwigPyPacked), /* tp_basicsize */ + 0, /* tp_itemsize */ + (destructor)SwigPyPacked_dealloc, /* tp_dealloc */ + (printfunc)SwigPyPacked_print, /* tp_print */ + (getattrfunc)0, /* tp_getattr */ + (setattrfunc)0, /* tp_setattr */ +#if PY_VERSION_HEX>=0x03000000 + 0, /* tp_reserved in 3.0.1 */ +#else + (cmpfunc)SwigPyPacked_compare, /* tp_compare */ +#endif + (reprfunc)SwigPyPacked_repr, /* tp_repr */ + 0, /* tp_as_number */ + 0, /* tp_as_sequence */ + 0, /* tp_as_mapping */ + (hashfunc)0, /* tp_hash */ + (ternaryfunc)0, /* tp_call */ + (reprfunc)SwigPyPacked_str, /* tp_str */ + PyObject_GenericGetAttr, /* tp_getattro */ + 0, /* tp_setattro */ + 0, /* tp_as_buffer */ + Py_TPFLAGS_DEFAULT, /* tp_flags */ + swigpacked_doc, /* tp_doc */ + 0, /* tp_traverse */ + 0, /* tp_clear */ + 0, /* tp_richcompare */ + 0, /* tp_weaklistoffset */ +#if PY_VERSION_HEX >= 0x02020000 + 0, /* tp_iter */ + 0, /* tp_iternext */ + 0, /* tp_methods */ + 0, /* tp_members */ + 0, /* tp_getset */ + 0, /* tp_base */ + 0, /* tp_dict */ + 0, /* tp_descr_get */ + 0, /* tp_descr_set */ + 0, /* tp_dictoffset */ + 0, /* tp_init */ + 0, /* tp_alloc */ + 0, /* tp_new */ + 0, /* tp_free */ + 0, /* tp_is_gc */ + 0, /* tp_bases */ + 0, /* tp_mro */ + 0, /* tp_cache */ + 0, /* tp_subclasses */ + 0, /* tp_weaklist */ +#endif +#if PY_VERSION_HEX >= 0x02030000 + 0, /* tp_del */ +#endif +#if PY_VERSION_HEX >= 0x02060000 + 0, /* tp_version_tag */ +#endif +#if PY_VERSION_HEX >= 0x03040000 + 0, /* tp_finalize */ +#endif +#ifdef COUNT_ALLOCS + 0, /* tp_allocs */ + 0, /* tp_frees */ + 0, /* tp_maxalloc */ +#if PY_VERSION_HEX >= 0x02050000 + 0, /* tp_prev */ +#endif + 0 /* tp_next */ +#endif + }; + swigpypacked_type = tmp; + type_init = 1; +#if PY_VERSION_HEX < 0x02020000 + swigpypacked_type.ob_type = &PyType_Type; +#else + if (PyType_Ready(&swigpypacked_type) < 0) + return NULL; +#endif + } + return &swigpypacked_type; +} + +SWIGRUNTIME PyObject * +SwigPyPacked_New(void *ptr, size_t size, swig_type_info *ty) +{ + SwigPyPacked *sobj = PyObject_NEW(SwigPyPacked, SwigPyPacked_type()); + if (sobj) { + void *pack = malloc(size); + if (pack) { + memcpy(pack, ptr, size); + sobj->pack = pack; + sobj->ty = ty; + sobj->size = size; + } else { + PyObject_DEL((PyObject *) sobj); + sobj = 0; + } + } + return (PyObject *) sobj; +} + +SWIGRUNTIME swig_type_info * +SwigPyPacked_UnpackData(PyObject *obj, void *ptr, size_t size) +{ + if (SwigPyPacked_Check(obj)) { + SwigPyPacked *sobj = (SwigPyPacked *)obj; + if (sobj->size != size) return 0; + memcpy(ptr, sobj->pack, size); + return sobj->ty; + } else { + return 0; + } +} + +/* ----------------------------------------------------------------------------- + * pointers/data manipulation + * ----------------------------------------------------------------------------- */ + +SWIGRUNTIMEINLINE PyObject * +_SWIG_This(void) +{ + return SWIG_Python_str_FromChar("this"); +} + +static PyObject *swig_this = NULL; + +SWIGRUNTIME PyObject * +SWIG_This(void) +{ + if (swig_this == NULL) + swig_this = _SWIG_This(); + return swig_this; +} + +/* #define SWIG_PYTHON_SLOW_GETSET_THIS */ + +/* TODO: I don't know how to implement the fast getset in Python 3 right now */ +#if PY_VERSION_HEX>=0x03000000 +#define SWIG_PYTHON_SLOW_GETSET_THIS +#endif + +SWIGRUNTIME SwigPyObject * +SWIG_Python_GetSwigThis(PyObject *pyobj) +{ + PyObject *obj; + + if (SwigPyObject_Check(pyobj)) + return (SwigPyObject *) pyobj; + +#ifdef SWIGPYTHON_BUILTIN + (void)obj; +# ifdef PyWeakref_CheckProxy + if (PyWeakref_CheckProxy(pyobj)) { + pyobj = PyWeakref_GET_OBJECT(pyobj); + if (pyobj && SwigPyObject_Check(pyobj)) + return (SwigPyObject*) pyobj; + } +# endif + return NULL; +#else + + obj = 0; + +#if (!defined(SWIG_PYTHON_SLOW_GETSET_THIS) && (PY_VERSION_HEX >= 0x02030000)) + if (PyInstance_Check(pyobj)) { + obj = _PyInstance_Lookup(pyobj, SWIG_This()); + } else { + PyObject **dictptr = _PyObject_GetDictPtr(pyobj); + if (dictptr != NULL) { + PyObject *dict = *dictptr; + obj = dict ? PyDict_GetItem(dict, SWIG_This()) : 0; + } else { +#ifdef PyWeakref_CheckProxy + if (PyWeakref_CheckProxy(pyobj)) { + PyObject *wobj = PyWeakref_GET_OBJECT(pyobj); + return wobj ? SWIG_Python_GetSwigThis(wobj) : 0; + } +#endif + obj = PyObject_GetAttr(pyobj,SWIG_This()); + if (obj) { + Py_DECREF(obj); + } else { + if (PyErr_Occurred()) PyErr_Clear(); + return 0; + } + } + } +#else + obj = PyObject_GetAttr(pyobj,SWIG_This()); + if (obj) { + Py_DECREF(obj); + } else { + if (PyErr_Occurred()) PyErr_Clear(); + return 0; + } +#endif + if (obj && !SwigPyObject_Check(obj)) { + /* a PyObject is called 'this', try to get the 'real this' + SwigPyObject from it */ + return SWIG_Python_GetSwigThis(obj); + } + return (SwigPyObject *)obj; +#endif +} + +/* Acquire a pointer value */ + +SWIGRUNTIME int +SWIG_Python_AcquirePtr(PyObject *obj, int own) { + if (own == SWIG_POINTER_OWN) { + SwigPyObject *sobj = SWIG_Python_GetSwigThis(obj); + if (sobj) { + int oldown = sobj->own; + sobj->own = own; + return oldown; + } + } + return 0; +} + +/* Convert a pointer value */ + +SWIGRUNTIME int +SWIG_Python_ConvertPtrAndOwn(PyObject *obj, void **ptr, swig_type_info *ty, int flags, int *own) { + int res; + SwigPyObject *sobj; + int implicit_conv = (flags & SWIG_POINTER_IMPLICIT_CONV) != 0; + + if (!obj) + return SWIG_ERROR; + if (obj == Py_None && !implicit_conv) { + if (ptr) + *ptr = 0; + return SWIG_OK; + } + + res = SWIG_ERROR; + + sobj = SWIG_Python_GetSwigThis(obj); + if (own) + *own = 0; + while (sobj) { + void *vptr = sobj->ptr; + if (ty) { + swig_type_info *to = sobj->ty; + if (to == ty) { + /* no type cast needed */ + if (ptr) *ptr = vptr; + break; + } else { + swig_cast_info *tc = SWIG_TypeCheck(to->name,ty); + if (!tc) { + sobj = (SwigPyObject *)sobj->next; + } else { + if (ptr) { + int newmemory = 0; + *ptr = SWIG_TypeCast(tc,vptr,&newmemory); + if (newmemory == SWIG_CAST_NEW_MEMORY) { + assert(own); /* badly formed typemap which will lead to a memory leak - it must set and use own to delete *ptr */ + if (own) + *own = *own | SWIG_CAST_NEW_MEMORY; + } + } + break; + } + } + } else { + if (ptr) *ptr = vptr; + break; + } + } + if (sobj) { + if (own) + *own = *own | sobj->own; + if (flags & SWIG_POINTER_DISOWN) { + sobj->own = 0; + } + res = SWIG_OK; + } else { + if (implicit_conv) { + SwigPyClientData *data = ty ? (SwigPyClientData *) ty->clientdata : 0; + if (data && !data->implicitconv) { + PyObject *klass = data->klass; + if (klass) { + PyObject *impconv; + data->implicitconv = 1; /* avoid recursion and call 'explicit' constructors*/ + impconv = SWIG_Python_CallFunctor(klass, obj); + data->implicitconv = 0; + if (PyErr_Occurred()) { + PyErr_Clear(); + impconv = 0; + } + if (impconv) { + SwigPyObject *iobj = SWIG_Python_GetSwigThis(impconv); + if (iobj) { + void *vptr; + res = SWIG_Python_ConvertPtrAndOwn((PyObject*)iobj, &vptr, ty, 0, 0); + if (SWIG_IsOK(res)) { + if (ptr) { + *ptr = vptr; + /* transfer the ownership to 'ptr' */ + iobj->own = 0; + res = SWIG_AddCast(res); + res = SWIG_AddNewMask(res); + } else { + res = SWIG_AddCast(res); + } + } + } + Py_DECREF(impconv); + } + } + } + } + if (!SWIG_IsOK(res) && obj == Py_None) { + if (ptr) + *ptr = 0; + if (PyErr_Occurred()) + PyErr_Clear(); + res = SWIG_OK; + } + } + return res; +} + +/* Convert a function ptr value */ + +SWIGRUNTIME int +SWIG_Python_ConvertFunctionPtr(PyObject *obj, void **ptr, swig_type_info *ty) { + if (!PyCFunction_Check(obj)) { + return SWIG_ConvertPtr(obj, ptr, ty, 0); + } else { + void *vptr = 0; + + /* here we get the method pointer for callbacks */ + const char *doc = (((PyCFunctionObject *)obj) -> m_ml -> ml_doc); + const char *desc = doc ? strstr(doc, "swig_ptr: ") : 0; + if (desc) + desc = ty ? SWIG_UnpackVoidPtr(desc + 10, &vptr, ty->name) : 0; + if (!desc) + return SWIG_ERROR; + if (ty) { + swig_cast_info *tc = SWIG_TypeCheck(desc,ty); + if (tc) { + int newmemory = 0; + *ptr = SWIG_TypeCast(tc,vptr,&newmemory); + assert(!newmemory); /* newmemory handling not yet implemented */ + } else { + return SWIG_ERROR; + } + } else { + *ptr = vptr; + } + return SWIG_OK; + } +} + +/* Convert a packed value value */ + +SWIGRUNTIME int +SWIG_Python_ConvertPacked(PyObject *obj, void *ptr, size_t sz, swig_type_info *ty) { + swig_type_info *to = SwigPyPacked_UnpackData(obj, ptr, sz); + if (!to) return SWIG_ERROR; + if (ty) { + if (to != ty) { + /* check type cast? */ + swig_cast_info *tc = SWIG_TypeCheck(to->name,ty); + if (!tc) return SWIG_ERROR; + } + } + return SWIG_OK; +} + +/* ----------------------------------------------------------------------------- + * Create a new pointer object + * ----------------------------------------------------------------------------- */ + +/* + Create a new instance object, without calling __init__, and set the + 'this' attribute. +*/ + +SWIGRUNTIME PyObject* +SWIG_Python_NewShadowInstance(SwigPyClientData *data, PyObject *swig_this) +{ +#if (PY_VERSION_HEX >= 0x02020000) + PyObject *inst = 0; + PyObject *newraw = data->newraw; + if (newraw) { + inst = PyObject_Call(newraw, data->newargs, NULL); + if (inst) { +#if !defined(SWIG_PYTHON_SLOW_GETSET_THIS) + PyObject **dictptr = _PyObject_GetDictPtr(inst); + if (dictptr != NULL) { + PyObject *dict = *dictptr; + if (dict == NULL) { + dict = PyDict_New(); + *dictptr = dict; + PyDict_SetItem(dict, SWIG_This(), swig_this); + } + } +#else + PyObject *key = SWIG_This(); + PyObject_SetAttr(inst, key, swig_this); +#endif + } + } else { +#if PY_VERSION_HEX >= 0x03000000 + inst = ((PyTypeObject*) data->newargs)->tp_new((PyTypeObject*) data->newargs, Py_None, Py_None); + if (inst) { + PyObject_SetAttr(inst, SWIG_This(), swig_this); + Py_TYPE(inst)->tp_flags &= ~Py_TPFLAGS_VALID_VERSION_TAG; + } +#else + PyObject *dict = PyDict_New(); + if (dict) { + PyDict_SetItem(dict, SWIG_This(), swig_this); + inst = PyInstance_NewRaw(data->newargs, dict); + Py_DECREF(dict); + } +#endif + } + return inst; +#else +#if (PY_VERSION_HEX >= 0x02010000) + PyObject *inst = 0; + PyObject *dict = PyDict_New(); + if (dict) { + PyDict_SetItem(dict, SWIG_This(), swig_this); + inst = PyInstance_NewRaw(data->newargs, dict); + Py_DECREF(dict); + } + return (PyObject *) inst; +#else + PyInstanceObject *inst = PyObject_NEW(PyInstanceObject, &PyInstance_Type); + if (inst == NULL) { + return NULL; + } + inst->in_class = (PyClassObject *)data->newargs; + Py_INCREF(inst->in_class); + inst->in_dict = PyDict_New(); + if (inst->in_dict == NULL) { + Py_DECREF(inst); + return NULL; + } +#ifdef Py_TPFLAGS_HAVE_WEAKREFS + inst->in_weakreflist = NULL; +#endif +#ifdef Py_TPFLAGS_GC + PyObject_GC_Init(inst); +#endif + PyDict_SetItem(inst->in_dict, SWIG_This(), swig_this); + return (PyObject *) inst; +#endif +#endif +} + +SWIGRUNTIME void +SWIG_Python_SetSwigThis(PyObject *inst, PyObject *swig_this) +{ + PyObject *dict; +#if (PY_VERSION_HEX >= 0x02020000) && !defined(SWIG_PYTHON_SLOW_GETSET_THIS) + PyObject **dictptr = _PyObject_GetDictPtr(inst); + if (dictptr != NULL) { + dict = *dictptr; + if (dict == NULL) { + dict = PyDict_New(); + *dictptr = dict; + } + PyDict_SetItem(dict, SWIG_This(), swig_this); + return; + } +#endif + dict = PyObject_GetAttrString(inst, (char*)"__dict__"); + PyDict_SetItem(dict, SWIG_This(), swig_this); + Py_DECREF(dict); +} + + +SWIGINTERN PyObject * +SWIG_Python_InitShadowInstance(PyObject *args) { + PyObject *obj[2]; + if (!SWIG_Python_UnpackTuple(args, "swiginit", 2, 2, obj)) { + return NULL; + } else { + SwigPyObject *sthis = SWIG_Python_GetSwigThis(obj[0]); + if (sthis) { + SwigPyObject_append((PyObject*) sthis, obj[1]); + } else { + SWIG_Python_SetSwigThis(obj[0], obj[1]); + } + return SWIG_Py_Void(); + } +} + +/* Create a new pointer object */ + +SWIGRUNTIME PyObject * +SWIG_Python_NewPointerObj(PyObject *self, void *ptr, swig_type_info *type, int flags) { + SwigPyClientData *clientdata; + PyObject * robj; + int own; + + if (!ptr) + return SWIG_Py_Void(); + + clientdata = type ? (SwigPyClientData *)(type->clientdata) : 0; + own = (flags & SWIG_POINTER_OWN) ? SWIG_POINTER_OWN : 0; + if (clientdata && clientdata->pytype) { + SwigPyObject *newobj; + if (flags & SWIG_BUILTIN_TP_INIT) { + newobj = (SwigPyObject*) self; + if (newobj->ptr) { + PyObject *next_self = clientdata->pytype->tp_alloc(clientdata->pytype, 0); + while (newobj->next) + newobj = (SwigPyObject *) newobj->next; + newobj->next = next_self; + newobj = (SwigPyObject *)next_self; +#ifdef SWIGPYTHON_BUILTIN + newobj->dict = 0; +#endif + } + } else { + newobj = PyObject_New(SwigPyObject, clientdata->pytype); +#ifdef SWIGPYTHON_BUILTIN + newobj->dict = 0; +#endif + } + if (newobj) { + newobj->ptr = ptr; + newobj->ty = type; + newobj->own = own; + newobj->next = 0; + return (PyObject*) newobj; + } + return SWIG_Py_Void(); + } + + assert(!(flags & SWIG_BUILTIN_TP_INIT)); + + robj = SwigPyObject_New(ptr, type, own); + if (robj && clientdata && !(flags & SWIG_POINTER_NOSHADOW)) { + PyObject *inst = SWIG_Python_NewShadowInstance(clientdata, robj); + Py_DECREF(robj); + robj = inst; + } + return robj; +} + +/* Create a new packed object */ + +SWIGRUNTIMEINLINE PyObject * +SWIG_Python_NewPackedObj(void *ptr, size_t sz, swig_type_info *type) { + return ptr ? SwigPyPacked_New((void *) ptr, sz, type) : SWIG_Py_Void(); +} + +/* -----------------------------------------------------------------------------* + * Get type list + * -----------------------------------------------------------------------------*/ + +#ifdef SWIG_LINK_RUNTIME +void *SWIG_ReturnGlobalTypeList(void *); +#endif + +SWIGRUNTIME swig_module_info * +SWIG_Python_GetModule(void *SWIGUNUSEDPARM(clientdata)) { + static void *type_pointer = (void *)0; + /* first check if module already created */ + if (!type_pointer) { +#ifdef SWIG_LINK_RUNTIME + type_pointer = SWIG_ReturnGlobalTypeList((void *)0); +#else +# ifdef SWIGPY_USE_CAPSULE + type_pointer = PyCapsule_Import(SWIGPY_CAPSULE_NAME, 0); +# else + type_pointer = PyCObject_Import((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION, + (char*)"type_pointer" SWIG_TYPE_TABLE_NAME); +# endif + if (PyErr_Occurred()) { + PyErr_Clear(); + type_pointer = (void *)0; + } +#endif + } + return (swig_module_info *) type_pointer; +} + +#if PY_MAJOR_VERSION < 2 +/* PyModule_AddObject function was introduced in Python 2.0. The following function + is copied out of Python/modsupport.c in python version 2.3.4 */ +SWIGINTERN int +PyModule_AddObject(PyObject *m, char *name, PyObject *o) +{ + PyObject *dict; + if (!PyModule_Check(m)) { + PyErr_SetString(PyExc_TypeError, "PyModule_AddObject() needs module as first arg"); + return SWIG_ERROR; + } + if (!o) { + PyErr_SetString(PyExc_TypeError, "PyModule_AddObject() needs non-NULL value"); + return SWIG_ERROR; + } + + dict = PyModule_GetDict(m); + if (dict == NULL) { + /* Internal error -- modules must have a dict! */ + PyErr_Format(PyExc_SystemError, "module '%s' has no __dict__", + PyModule_GetName(m)); + return SWIG_ERROR; + } + if (PyDict_SetItemString(dict, name, o)) + return SWIG_ERROR; + Py_DECREF(o); + return SWIG_OK; +} +#endif + +SWIGRUNTIME void +#ifdef SWIGPY_USE_CAPSULE +SWIG_Python_DestroyModule(PyObject *obj) +#else +SWIG_Python_DestroyModule(void *vptr) +#endif +{ +#ifdef SWIGPY_USE_CAPSULE + swig_module_info *swig_module = (swig_module_info *) PyCapsule_GetPointer(obj, SWIGPY_CAPSULE_NAME); +#else + swig_module_info *swig_module = (swig_module_info *) vptr; +#endif + swig_type_info **types = swig_module->types; + size_t i; + for (i =0; i < swig_module->size; ++i) { + swig_type_info *ty = types[i]; + if (ty->owndata) { + SwigPyClientData *data = (SwigPyClientData *) ty->clientdata; + if (data) SwigPyClientData_Del(data); + } + } + Py_DECREF(SWIG_This()); + swig_this = NULL; +} + +SWIGRUNTIME void +SWIG_Python_SetModule(swig_module_info *swig_module) { +#if PY_VERSION_HEX >= 0x03000000 + /* Add a dummy module object into sys.modules */ + PyObject *module = PyImport_AddModule((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION); +#else + static PyMethodDef swig_empty_runtime_method_table[] = { {NULL, NULL, 0, NULL} }; /* Sentinel */ + PyObject *module = Py_InitModule((char*)"swig_runtime_data" SWIG_RUNTIME_VERSION, swig_empty_runtime_method_table); +#endif +#ifdef SWIGPY_USE_CAPSULE + PyObject *pointer = PyCapsule_New((void *) swig_module, SWIGPY_CAPSULE_NAME, SWIG_Python_DestroyModule); + if (pointer && module) { + PyModule_AddObject(module, (char*)"type_pointer_capsule" SWIG_TYPE_TABLE_NAME, pointer); + } else { + Py_XDECREF(pointer); + } +#else + PyObject *pointer = PyCObject_FromVoidPtr((void *) swig_module, SWIG_Python_DestroyModule); + if (pointer && module) { + PyModule_AddObject(module, (char*)"type_pointer" SWIG_TYPE_TABLE_NAME, pointer); + } else { + Py_XDECREF(pointer); + } +#endif +} + +/* The python cached type query */ +SWIGRUNTIME PyObject * +SWIG_Python_TypeCache(void) { + static PyObject *SWIG_STATIC_POINTER(cache) = PyDict_New(); + return cache; +} + +SWIGRUNTIME swig_type_info * +SWIG_Python_TypeQuery(const char *type) +{ + PyObject *cache = SWIG_Python_TypeCache(); + PyObject *key = SWIG_Python_str_FromChar(type); + PyObject *obj = PyDict_GetItem(cache, key); + swig_type_info *descriptor; + if (obj) { +#ifdef SWIGPY_USE_CAPSULE + descriptor = (swig_type_info *) PyCapsule_GetPointer(obj, NULL); +#else + descriptor = (swig_type_info *) PyCObject_AsVoidPtr(obj); +#endif + } else { + swig_module_info *swig_module = SWIG_GetModule(0); + descriptor = SWIG_TypeQueryModule(swig_module, swig_module, type); + if (descriptor) { +#ifdef SWIGPY_USE_CAPSULE + obj = PyCapsule_New((void*) descriptor, NULL, NULL); +#else + obj = PyCObject_FromVoidPtr(descriptor, NULL); +#endif + PyDict_SetItem(cache, key, obj); + Py_DECREF(obj); + } + } + Py_DECREF(key); + return descriptor; +} + +/* + For backward compatibility only +*/ +#define SWIG_POINTER_EXCEPTION 0 +#define SWIG_arg_fail(arg) SWIG_Python_ArgFail(arg) +#define SWIG_MustGetPtr(p, type, argnum, flags) SWIG_Python_MustGetPtr(p, type, argnum, flags) + +SWIGRUNTIME int +SWIG_Python_AddErrMesg(const char* mesg, int infront) +{ + if (PyErr_Occurred()) { + PyObject *type = 0; + PyObject *value = 0; + PyObject *traceback = 0; + PyErr_Fetch(&type, &value, &traceback); + if (value) { + char *tmp; + PyObject *old_str = PyObject_Str(value); + Py_XINCREF(type); + PyErr_Clear(); + if (infront) { + PyErr_Format(type, "%s %s", mesg, tmp = SWIG_Python_str_AsChar(old_str)); + } else { + PyErr_Format(type, "%s %s", tmp = SWIG_Python_str_AsChar(old_str), mesg); + } + SWIG_Python_str_DelForPy3(tmp); + Py_DECREF(old_str); + } + return 1; + } else { + return 0; + } +} + +SWIGRUNTIME int +SWIG_Python_ArgFail(int argnum) +{ + if (PyErr_Occurred()) { + /* add information about failing argument */ + char mesg[256]; + PyOS_snprintf(mesg, sizeof(mesg), "argument number %d:", argnum); + return SWIG_Python_AddErrMesg(mesg, 1); + } else { + return 0; + } +} + +SWIGRUNTIMEINLINE const char * +SwigPyObject_GetDesc(PyObject *self) +{ + SwigPyObject *v = (SwigPyObject *)self; + swig_type_info *ty = v ? v->ty : 0; + return ty ? ty->str : ""; +} + +SWIGRUNTIME void +SWIG_Python_TypeError(const char *type, PyObject *obj) +{ + if (type) { +#if defined(SWIG_COBJECT_TYPES) + if (obj && SwigPyObject_Check(obj)) { + const char *otype = (const char *) SwigPyObject_GetDesc(obj); + if (otype) { + PyErr_Format(PyExc_TypeError, "a '%s' is expected, 'SwigPyObject(%s)' is received", + type, otype); + return; + } + } else +#endif + { + const char *otype = (obj ? obj->ob_type->tp_name : 0); + if (otype) { + PyObject *str = PyObject_Str(obj); + const char *cstr = str ? SWIG_Python_str_AsChar(str) : 0; + if (cstr) { + PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s(%s)' is received", + type, otype, cstr); + SWIG_Python_str_DelForPy3(cstr); + } else { + PyErr_Format(PyExc_TypeError, "a '%s' is expected, '%s' is received", + type, otype); + } + Py_XDECREF(str); + return; + } + } + PyErr_Format(PyExc_TypeError, "a '%s' is expected", type); + } else { + PyErr_Format(PyExc_TypeError, "unexpected type is received"); + } +} + + +/* Convert a pointer value, signal an exception on a type mismatch */ +SWIGRUNTIME void * +SWIG_Python_MustGetPtr(PyObject *obj, swig_type_info *ty, int SWIGUNUSEDPARM(argnum), int flags) { + void *result; + if (SWIG_Python_ConvertPtr(obj, &result, ty, flags) == -1) { + PyErr_Clear(); +#if SWIG_POINTER_EXCEPTION + if (flags) { + SWIG_Python_TypeError(SWIG_TypePrettyName(ty), obj); + SWIG_Python_ArgFail(argnum); + } +#endif + } + return result; +} + +#ifdef SWIGPYTHON_BUILTIN +SWIGRUNTIME int +SWIG_Python_NonDynamicSetAttr(PyObject *obj, PyObject *name, PyObject *value) { + PyTypeObject *tp = obj->ob_type; + PyObject *descr; + PyObject *encoded_name; + descrsetfunc f; + int res = -1; + +# ifdef Py_USING_UNICODE + if (PyString_Check(name)) { + name = PyUnicode_Decode(PyString_AsString(name), PyString_Size(name), NULL, NULL); + if (!name) + return -1; + } else if (!PyUnicode_Check(name)) +# else + if (!PyString_Check(name)) +# endif + { + PyErr_Format(PyExc_TypeError, "attribute name must be string, not '%.200s'", name->ob_type->tp_name); + return -1; + } else { + Py_INCREF(name); + } + + if (!tp->tp_dict) { + if (PyType_Ready(tp) < 0) + goto done; + } + + descr = _PyType_Lookup(tp, name); + f = NULL; + if (descr != NULL) + f = descr->ob_type->tp_descr_set; + if (!f) { + if (PyString_Check(name)) { + encoded_name = name; + Py_INCREF(name); + } else { + encoded_name = PyUnicode_AsUTF8String(name); + } + PyErr_Format(PyExc_AttributeError, "'%.100s' object has no attribute '%.200s'", tp->tp_name, PyString_AsString(encoded_name)); + Py_DECREF(encoded_name); + } else { + res = f(descr, obj, value); + } + + done: + Py_DECREF(name); + return res; +} +#endif + + +#ifdef __cplusplus +} +#endif + + + +#define SWIG_exception_fail(code, msg) do { SWIG_Error(code, msg); SWIG_fail; } while(0) + +#define SWIG_contract_assert(expr, msg) if (!(expr)) { SWIG_Error(SWIG_RuntimeError, msg); SWIG_fail; } else + + + + #define SWIG_exception(code, msg) do { SWIG_Error(code, msg); SWIG_fail;; } while(0) + + +/* -------- TYPES TABLE (BEGIN) -------- */ + +#define SWIGTYPE_p_allocator_type swig_types[0] +#define SWIGTYPE_p_char swig_types[1] +#define SWIGTYPE_p_difference_type swig_types[2] +#define SWIGTYPE_p_p_PyObject swig_types[3] +#define SWIGTYPE_p_size_type swig_types[4] +#define SWIGTYPE_p_std__allocatorT_double_t swig_types[5] +#define SWIGTYPE_p_std__invalid_argument swig_types[6] +#define SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t swig_types[7] +#define SWIGTYPE_p_swig__SwigPyIterator swig_types[8] +#define SWIGTYPE_p_value_type swig_types[9] +static swig_type_info *swig_types[11]; +static swig_module_info swig_module = {swig_types, 10, 0, 0, 0, 0}; +#define SWIG_TypeQuery(name) SWIG_TypeQueryModule(&swig_module, &swig_module, name) +#define SWIG_MangledTypeQuery(name) SWIG_MangledTypeQueryModule(&swig_module, &swig_module, name) + +/* -------- TYPES TABLE (END) -------- */ + +#if (PY_VERSION_HEX <= 0x02000000) +# if !defined(SWIG_PYTHON_CLASSIC) +# error "This python version requires swig to be run with the '-classic' option" +# endif +#endif + +/*----------------------------------------------- + @(target):= _polyiou.so + ------------------------------------------------*/ +#if PY_VERSION_HEX >= 0x03000000 +# define SWIG_init PyInit__polyiou + +#else +# define SWIG_init init_polyiou + +#endif +#define SWIG_name "_polyiou" + +#define SWIGVERSION 0x030008 +#define SWIG_VERSION SWIGVERSION + + +#define SWIG_as_voidptr(a) const_cast< void * >(static_cast< const void * >(a)) +#define SWIG_as_voidptrptr(a) ((void)SWIG_as_voidptr(*a),reinterpret_cast< void** >(a)) + + +#include + + +namespace swig { + class SwigPtr_PyObject { + protected: + PyObject *_obj; + + public: + SwigPtr_PyObject() :_obj(0) + { + } + + SwigPtr_PyObject(const SwigPtr_PyObject& item) : _obj(item._obj) + { + SWIG_PYTHON_THREAD_BEGIN_BLOCK; + Py_XINCREF(_obj); + SWIG_PYTHON_THREAD_END_BLOCK; + } + + SwigPtr_PyObject(PyObject *obj, bool initial_ref = true) :_obj(obj) + { + if (initial_ref) { + SWIG_PYTHON_THREAD_BEGIN_BLOCK; + Py_XINCREF(_obj); + SWIG_PYTHON_THREAD_END_BLOCK; + } + } + + SwigPtr_PyObject & operator=(const SwigPtr_PyObject& item) + { + SWIG_PYTHON_THREAD_BEGIN_BLOCK; + Py_XINCREF(item._obj); + Py_XDECREF(_obj); + _obj = item._obj; + SWIG_PYTHON_THREAD_END_BLOCK; + return *this; + } + + ~SwigPtr_PyObject() + { + SWIG_PYTHON_THREAD_BEGIN_BLOCK; + Py_XDECREF(_obj); + SWIG_PYTHON_THREAD_END_BLOCK; + } + + operator PyObject *() const + { + return _obj; + } + + PyObject *operator->() const + { + return _obj; + } + }; +} + + +namespace swig { + struct SwigVar_PyObject : SwigPtr_PyObject { + SwigVar_PyObject(PyObject* obj = 0) : SwigPtr_PyObject(obj, false) { } + + SwigVar_PyObject & operator = (PyObject* obj) + { + Py_XDECREF(_obj); + _obj = obj; + return *this; + } + }; +} + + +#include + +#if PY_VERSION_HEX >= 0x03020000 +# define SWIGPY_SLICE_ARG(obj) ((PyObject*) (obj)) +#else +# define SWIGPY_SLICE_ARG(obj) ((PySliceObject*) (obj)) +#endif + + +#include + + +#if defined(__GNUC__) +# if __GNUC__ == 2 && __GNUC_MINOR <= 96 +# define SWIG_STD_NOMODERN_STL +# endif +#endif + + +#include + + +#include + + +namespace swig { + struct stop_iteration { + }; + + struct SwigPyIterator { + private: + SwigPtr_PyObject _seq; + + protected: + SwigPyIterator(PyObject *seq) : _seq(seq) + { + } + + public: + virtual ~SwigPyIterator() {} + + // Access iterator method, required by Python + virtual PyObject *value() const = 0; + + // Forward iterator method, required by Python + virtual SwigPyIterator *incr(size_t n = 1) = 0; + + // Backward iterator method, very common in C++, but not required in Python + virtual SwigPyIterator *decr(size_t /*n*/ = 1) + { + throw stop_iteration(); + } + + // Random access iterator methods, but not required in Python + virtual ptrdiff_t distance(const SwigPyIterator &/*x*/) const + { + throw std::invalid_argument("operation not supported"); + } + + virtual bool equal (const SwigPyIterator &/*x*/) const + { + throw std::invalid_argument("operation not supported"); + } + + // C++ common/needed methods + virtual SwigPyIterator *copy() const = 0; + + PyObject *next() + { + SWIG_PYTHON_THREAD_BEGIN_BLOCK; // disable threads + PyObject *obj = value(); + incr(); + SWIG_PYTHON_THREAD_END_BLOCK; // re-enable threads + return obj; + } + + /* Make an alias for Python 3.x */ + PyObject *__next__() + { + return next(); + } + + PyObject *previous() + { + SWIG_PYTHON_THREAD_BEGIN_BLOCK; // disable threads + decr(); + PyObject *obj = value(); + SWIG_PYTHON_THREAD_END_BLOCK; // re-enable threads + return obj; + } + + SwigPyIterator *advance(ptrdiff_t n) + { + return (n > 0) ? incr(n) : decr(-n); + } + + bool operator == (const SwigPyIterator& x) const + { + return equal(x); + } + + bool operator != (const SwigPyIterator& x) const + { + return ! operator==(x); + } + + SwigPyIterator& operator += (ptrdiff_t n) + { + return *advance(n); + } + + SwigPyIterator& operator -= (ptrdiff_t n) + { + return *advance(-n); + } + + SwigPyIterator* operator + (ptrdiff_t n) const + { + return copy()->advance(n); + } + + SwigPyIterator* operator - (ptrdiff_t n) const + { + return copy()->advance(-n); + } + + ptrdiff_t operator - (const SwigPyIterator& x) const + { + return x.distance(*this); + } + + static swig_type_info* descriptor() { + static int init = 0; + static swig_type_info* desc = 0; + if (!init) { + desc = SWIG_TypeQuery("swig::SwigPyIterator *"); + init = 1; + } + return desc; + } + }; + +#if defined(SWIGPYTHON_BUILTIN) + inline PyObject* make_output_iterator_builtin (PyObject *pyself) + { + Py_INCREF(pyself); + return pyself; + } +#endif +} + + +SWIGINTERN int +SWIG_AsVal_double (PyObject *obj, double *val) +{ + int res = SWIG_TypeError; + if (PyFloat_Check(obj)) { + if (val) *val = PyFloat_AsDouble(obj); + return SWIG_OK; +#if PY_VERSION_HEX < 0x03000000 + } else if (PyInt_Check(obj)) { + if (val) *val = PyInt_AsLong(obj); + return SWIG_OK; +#endif + } else if (PyLong_Check(obj)) { + double v = PyLong_AsDouble(obj); + if (!PyErr_Occurred()) { + if (val) *val = v; + return SWIG_OK; + } else { + PyErr_Clear(); + } + } +#ifdef SWIG_PYTHON_CAST_MODE + { + int dispatch = 0; + double d = PyFloat_AsDouble(obj); + if (!PyErr_Occurred()) { + if (val) *val = d; + return SWIG_AddCast(SWIG_OK); + } else { + PyErr_Clear(); + } + if (!dispatch) { + long v = PyLong_AsLong(obj); + if (!PyErr_Occurred()) { + if (val) *val = v; + return SWIG_AddCast(SWIG_AddCast(SWIG_OK)); + } else { + PyErr_Clear(); + } + } + } +#endif + return res; +} + + +#include + + +#include + + +SWIGINTERNINLINE int +SWIG_CanCastAsInteger(double *d, double min, double max) { + double x = *d; + if ((min <= x && x <= max)) { + double fx = floor(x); + double cx = ceil(x); + double rd = ((x - fx) < 0.5) ? fx : cx; /* simple rint */ + if ((errno == EDOM) || (errno == ERANGE)) { + errno = 0; + } else { + double summ, reps, diff; + if (rd < x) { + diff = x - rd; + } else if (rd > x) { + diff = rd - x; + } else { + return 1; + } + summ = rd + x; + reps = diff/summ; + if (reps < 8*DBL_EPSILON) { + *d = rd; + return 1; + } + } + } + return 0; +} + + +SWIGINTERN int +SWIG_AsVal_unsigned_SS_long (PyObject *obj, unsigned long *val) +{ +#if PY_VERSION_HEX < 0x03000000 + if (PyInt_Check(obj)) { + long v = PyInt_AsLong(obj); + if (v >= 0) { + if (val) *val = v; + return SWIG_OK; + } else { + return SWIG_OverflowError; + } + } else +#endif + if (PyLong_Check(obj)) { + unsigned long v = PyLong_AsUnsignedLong(obj); + if (!PyErr_Occurred()) { + if (val) *val = v; + return SWIG_OK; + } else { + PyErr_Clear(); + return SWIG_OverflowError; + } + } +#ifdef SWIG_PYTHON_CAST_MODE + { + int dispatch = 0; + unsigned long v = PyLong_AsUnsignedLong(obj); + if (!PyErr_Occurred()) { + if (val) *val = v; + return SWIG_AddCast(SWIG_OK); + } else { + PyErr_Clear(); + } + if (!dispatch) { + double d; + int res = SWIG_AddCast(SWIG_AsVal_double (obj,&d)); + if (SWIG_IsOK(res) && SWIG_CanCastAsInteger(&d, 0, ULONG_MAX)) { + if (val) *val = (unsigned long)(d); + return res; + } + } + } +#endif + return SWIG_TypeError; +} + + +SWIGINTERNINLINE int +SWIG_AsVal_size_t (PyObject * obj, size_t *val) +{ + unsigned long v; + int res = SWIG_AsVal_unsigned_SS_long (obj, val ? &v : 0); + if (SWIG_IsOK(res) && val) *val = static_cast< size_t >(v); + return res; +} + + + #define SWIG_From_long PyLong_FromLong + + +SWIGINTERNINLINE PyObject * +SWIG_From_ptrdiff_t (ptrdiff_t value) +{ + return SWIG_From_long (static_cast< long >(value)); +} + + +SWIGINTERNINLINE PyObject* + SWIG_From_bool (bool value) +{ + return PyBool_FromLong(value ? 1 : 0); +} + + +SWIGINTERN int +SWIG_AsVal_long (PyObject *obj, long* val) +{ +#if PY_VERSION_HEX < 0x03000000 + if (PyInt_Check(obj)) { + if (val) *val = PyInt_AsLong(obj); + return SWIG_OK; + } else +#endif + if (PyLong_Check(obj)) { + long v = PyLong_AsLong(obj); + if (!PyErr_Occurred()) { + if (val) *val = v; + return SWIG_OK; + } else { + PyErr_Clear(); + return SWIG_OverflowError; + } + } +#ifdef SWIG_PYTHON_CAST_MODE + { + int dispatch = 0; + long v = PyInt_AsLong(obj); + if (!PyErr_Occurred()) { + if (val) *val = v; + return SWIG_AddCast(SWIG_OK); + } else { + PyErr_Clear(); + } + if (!dispatch) { + double d; + int res = SWIG_AddCast(SWIG_AsVal_double (obj,&d)); + if (SWIG_IsOK(res) && SWIG_CanCastAsInteger(&d, LONG_MIN, LONG_MAX)) { + if (val) *val = (long)(d); + return res; + } + } + } +#endif + return SWIG_TypeError; +} + + +SWIGINTERNINLINE int +SWIG_AsVal_ptrdiff_t (PyObject * obj, ptrdiff_t *val) +{ + long v; + int res = SWIG_AsVal_long (obj, val ? &v : 0); + if (SWIG_IsOK(res) && val) *val = static_cast< ptrdiff_t >(v); + return res; +} + + +#include + + +#include + + +namespace swig { + template + struct noconst_traits { + typedef Type noconst_type; + }; + + template + struct noconst_traits { + typedef Type noconst_type; + }; + + /* + type categories + */ + struct pointer_category { }; + struct value_category { }; + + /* + General traits that provides type_name and type_info + */ + template struct traits { }; + + template + inline const char* type_name() { + return traits::noconst_type >::type_name(); + } + + template + struct traits_info { + static swig_type_info *type_query(std::string name) { + name += " *"; + return SWIG_TypeQuery(name.c_str()); + } + static swig_type_info *type_info() { + static swig_type_info *info = type_query(type_name()); + return info; + } + }; + + template + inline swig_type_info *type_info() { + return traits_info::type_info(); + } + + /* + Partial specialization for pointers + */ + template struct traits { + typedef pointer_category category; + static std::string make_ptr_name(const char* name) { + std::string ptrname = name; + ptrname += " *"; + return ptrname; + } + static const char* type_name() { + static std::string name = make_ptr_name(swig::type_name()); + return name.c_str(); + } + }; + + template + struct traits_as { }; + + template + struct traits_check { }; + +} + + +namespace swig { + /* + Traits that provides the from method + */ + template struct traits_from_ptr { + static PyObject *from(Type *val, int owner = 0) { + return SWIG_InternalNewPointerObj(val, type_info(), owner); + } + }; + + template struct traits_from { + static PyObject *from(const Type& val) { + return traits_from_ptr::from(new Type(val), 1); + } + }; + + template struct traits_from { + static PyObject *from(Type* val) { + return traits_from_ptr::from(val, 0); + } + }; + + template struct traits_from { + static PyObject *from(const Type* val) { + return traits_from_ptr::from(const_cast(val), 0); + } + }; + + + template + inline PyObject *from(const Type& val) { + return traits_from::from(val); + } + + template + inline PyObject *from_ptr(Type* val, int owner) { + return traits_from_ptr::from(val, owner); + } + + /* + Traits that provides the asval/as/check method + */ + template + struct traits_asptr { + static int asptr(PyObject *obj, Type **val) { + Type *p; + int res = SWIG_ConvertPtr(obj, (void**)&p, type_info(), 0); + if (SWIG_IsOK(res)) { + if (val) *val = p; + } + return res; + } + }; + + template + inline int asptr(PyObject *obj, Type **vptr) { + return traits_asptr::asptr(obj, vptr); + } + + template + struct traits_asval { + static int asval(PyObject *obj, Type *val) { + if (val) { + Type *p = 0; + int res = traits_asptr::asptr(obj, &p); + if (!SWIG_IsOK(res)) return res; + if (p) { + typedef typename noconst_traits::noconst_type noconst_type; + *(const_cast(val)) = *p; + if (SWIG_IsNewObj(res)){ + delete p; + res = SWIG_DelNewMask(res); + } + return res; + } else { + return SWIG_ERROR; + } + } else { + return traits_asptr::asptr(obj, (Type **)(0)); + } + } + }; + + template struct traits_asval { + static int asval(PyObject *obj, Type **val) { + if (val) { + typedef typename noconst_traits::noconst_type noconst_type; + noconst_type *p = 0; + int res = traits_asptr::asptr(obj, &p); + if (SWIG_IsOK(res)) { + *(const_cast(val)) = p; + } + return res; + } else { + return traits_asptr::asptr(obj, (Type **)(0)); + } + } + }; + + template + inline int asval(PyObject *obj, Type *val) { + return traits_asval::asval(obj, val); + } + + template + struct traits_as { + static Type as(PyObject *obj, bool throw_error) { + Type v; + int res = asval(obj, &v); + if (!obj || !SWIG_IsOK(res)) { + if (!PyErr_Occurred()) { + ::SWIG_Error(SWIG_TypeError, swig::type_name()); + } + if (throw_error) throw std::invalid_argument("bad type"); + } + return v; + } + }; + + template + struct traits_as { + static Type as(PyObject *obj, bool throw_error) { + Type *v = 0; + int res = (obj ? traits_asptr::asptr(obj, &v) : SWIG_ERROR); + if (SWIG_IsOK(res) && v) { + if (SWIG_IsNewObj(res)) { + Type r(*v); + delete v; + return r; + } else { + return *v; + } + } else { + // Uninitialized return value, no Type() constructor required. + static Type *v_def = (Type*) malloc(sizeof(Type)); + if (!PyErr_Occurred()) { + SWIG_Error(SWIG_TypeError, swig::type_name()); + } + if (throw_error) throw std::invalid_argument("bad type"); + memset(v_def,0,sizeof(Type)); + return *v_def; + } + } + }; + + template + struct traits_as { + static Type* as(PyObject *obj, bool throw_error) { + Type *v = 0; + int res = (obj ? traits_asptr::asptr(obj, &v) : SWIG_ERROR); + if (SWIG_IsOK(res)) { + return v; + } else { + if (!PyErr_Occurred()) { + SWIG_Error(SWIG_TypeError, swig::type_name()); + } + if (throw_error) throw std::invalid_argument("bad type"); + return 0; + } + } + }; + + template + inline Type as(PyObject *obj, bool te = false) { + return traits_as::category>::as(obj, te); + } + + template + struct traits_check { + static bool check(PyObject *obj) { + int res = obj ? asval(obj, (Type *)(0)) : SWIG_ERROR; + return SWIG_IsOK(res) ? true : false; + } + }; + + template + struct traits_check { + static bool check(PyObject *obj) { + int res = obj ? asptr(obj, (Type **)(0)) : SWIG_ERROR; + return SWIG_IsOK(res) ? true : false; + } + }; + + template + inline bool check(PyObject *obj) { + return traits_check::category>::check(obj); + } +} + + +#include + +namespace std { + template <> + struct less : public binary_function + { + bool + operator()(PyObject * v, PyObject *w) const + { + bool res; + SWIG_PYTHON_THREAD_BEGIN_BLOCK; + res = PyObject_RichCompareBool(v, w, Py_LT) ? true : false; + /* This may fall into a case of inconsistent + eg. ObjA > ObjX > ObjB + but ObjA < ObjB + */ + if( PyErr_Occurred() && PyErr_ExceptionMatches(PyExc_TypeError) ) + { + /* Objects can't be compared, this mostly occurred in Python 3.0 */ + /* Compare their ptr directly for a workaround */ + res = (v < w); + PyErr_Clear(); + } + SWIG_PYTHON_THREAD_END_BLOCK; + return res; + } + }; + + template <> + struct less : public binary_function + { + bool + operator()(const swig::SwigPtr_PyObject& v, const swig::SwigPtr_PyObject& w) const + { + return std::less()(v, w); + } + }; + + template <> + struct less : public binary_function + { + bool + operator()(const swig::SwigVar_PyObject& v, const swig::SwigVar_PyObject& w) const + { + return std::less()(v, w); + } + }; + +} + +namespace swig { + template <> struct traits { + typedef value_category category; + static const char* type_name() { return "PyObject *"; } + }; + + template <> struct traits_asval { + typedef PyObject * value_type; + static int asval(PyObject *obj, value_type *val) { + if (val) *val = obj; + return SWIG_OK; + } + }; + + template <> + struct traits_check { + static bool check(PyObject *) { + return true; + } + }; + + template <> struct traits_from { + typedef PyObject * value_type; + static PyObject *from(const value_type& val) { + Py_XINCREF(val); + return val; + } + }; + +} + +namespace swig { + template + inline size_t + check_index(Difference i, size_t size, bool insert = false) { + if ( i < 0 ) { + if ((size_t) (-i) <= size) + return (size_t) (i + size); + } else if ( (size_t) i < size ) { + return (size_t) i; + } else if (insert && ((size_t) i == size)) { + return size; + } + throw std::out_of_range("index out of range"); + } + + template + void + slice_adjust(Difference i, Difference j, Py_ssize_t step, size_t size, Difference &ii, Difference &jj, bool insert = false) { + if (step == 0) { + throw std::invalid_argument("slice step cannot be zero"); + } else if (step > 0) { + // Required range: 0 <= i < size, 0 <= j < size + if (i < 0) { + ii = 0; + } else if (i < (Difference)size) { + ii = i; + } else if (insert && (i >= (Difference)size)) { + ii = (Difference)size; + } + if ( j < 0 ) { + jj = 0; + } else { + jj = (j < (Difference)size) ? j : (Difference)size; + } + } else { + // Required range: -1 <= i < size-1, -1 <= j < size-1 + if (i < -1) { + ii = -1; + } else if (i < (Difference) size) { + ii = i; + } else if (i >= (Difference)(size-1)) { + ii = (Difference)(size-1); + } + if (j < -1) { + jj = -1; + } else { + jj = (j < (Difference)size ) ? j : (Difference)(size-1); + } + } + } + + template + inline typename Sequence::iterator + getpos(Sequence* self, Difference i) { + typename Sequence::iterator pos = self->begin(); + std::advance(pos, check_index(i,self->size())); + return pos; + } + + template + inline typename Sequence::const_iterator + cgetpos(const Sequence* self, Difference i) { + typename Sequence::const_iterator pos = self->begin(); + std::advance(pos, check_index(i,self->size())); + return pos; + } + + template + inline void + erase(Sequence* seq, const typename Sequence::iterator& position) { + seq->erase(position); + } + + template + inline Sequence* + getslice(const Sequence* self, Difference i, Difference j, Py_ssize_t step) { + typename Sequence::size_type size = self->size(); + Difference ii = 0; + Difference jj = 0; + swig::slice_adjust(i, j, step, size, ii, jj); + + if (step > 0) { + typename Sequence::const_iterator sb = self->begin(); + typename Sequence::const_iterator se = self->begin(); + std::advance(sb,ii); + std::advance(se,jj); + if (step == 1) { + return new Sequence(sb, se); + } else { + Sequence *sequence = new Sequence(); + typename Sequence::const_iterator it = sb; + while (it!=se) { + sequence->push_back(*it); + for (Py_ssize_t c=0; c jj) { + typename Sequence::const_reverse_iterator sb = self->rbegin(); + typename Sequence::const_reverse_iterator se = self->rbegin(); + std::advance(sb,size-ii-1); + std::advance(se,size-jj-1); + typename Sequence::const_reverse_iterator it = sb; + while (it!=se) { + sequence->push_back(*it); + for (Py_ssize_t c=0; c<-step && it!=se; ++c) + it++; + } + } + return sequence; + } + } + + template + inline void + setslice(Sequence* self, Difference i, Difference j, Py_ssize_t step, const InputSeq& is = InputSeq()) { + typename Sequence::size_type size = self->size(); + Difference ii = 0; + Difference jj = 0; + swig::slice_adjust(i, j, step, size, ii, jj, true); + if (step > 0) { + if (jj < ii) + jj = ii; + if (step == 1) { + size_t ssize = jj - ii; + if (ssize <= is.size()) { + // expanding/staying the same size + typename Sequence::iterator sb = self->begin(); + typename InputSeq::const_iterator isit = is.begin(); + std::advance(sb,ii); + std::advance(isit, jj - ii); + self->insert(std::copy(is.begin(), isit, sb), isit, is.end()); + } else { + // shrinking + typename Sequence::iterator sb = self->begin(); + typename Sequence::iterator se = self->begin(); + std::advance(sb,ii); + std::advance(se,jj); + self->erase(sb,se); + sb = self->begin(); + std::advance(sb,ii); + self->insert(sb, is.begin(), is.end()); + } + } else { + size_t replacecount = (jj - ii + step - 1) / step; + if (is.size() != replacecount) { + char msg[1024]; + sprintf(msg, "attempt to assign sequence of size %lu to extended slice of size %lu", (unsigned long)is.size(), (unsigned long)replacecount); + throw std::invalid_argument(msg); + } + typename Sequence::const_iterator isit = is.begin(); + typename Sequence::iterator it = self->begin(); + std::advance(it,ii); + for (size_t rc=0; rcend(); ++c) + it++; + } + } + } else { + if (jj > ii) + jj = ii; + size_t replacecount = (ii - jj - step - 1) / -step; + if (is.size() != replacecount) { + char msg[1024]; + sprintf(msg, "attempt to assign sequence of size %lu to extended slice of size %lu", (unsigned long)is.size(), (unsigned long)replacecount); + throw std::invalid_argument(msg); + } + typename Sequence::const_iterator isit = is.begin(); + typename Sequence::reverse_iterator it = self->rbegin(); + std::advance(it,size-ii-1); + for (size_t rc=0; rcrend(); ++c) + it++; + } + } + } + + template + inline void + delslice(Sequence* self, Difference i, Difference j, Py_ssize_t step) { + typename Sequence::size_type size = self->size(); + Difference ii = 0; + Difference jj = 0; + swig::slice_adjust(i, j, step, size, ii, jj, true); + if (step > 0) { + if (jj > ii) { + typename Sequence::iterator sb = self->begin(); + std::advance(sb,ii); + if (step == 1) { + typename Sequence::iterator se = self->begin(); + std::advance(se,jj); + self->erase(sb,se); + } else { + typename Sequence::iterator it = sb; + size_t delcount = (jj - ii + step - 1) / step; + while (delcount) { + it = self->erase(it); + for (Py_ssize_t c=0; c<(step-1) && it != self->end(); ++c) + it++; + delcount--; + } + } + } + } else { + if (ii > jj) { + typename Sequence::reverse_iterator sb = self->rbegin(); + std::advance(sb,size-ii-1); + typename Sequence::reverse_iterator it = sb; + size_t delcount = (ii - jj - step - 1) / -step; + while (delcount) { + it = typename Sequence::reverse_iterator(self->erase((++it).base())); + for (Py_ssize_t c=0; c<(-step-1) && it != self->rend(); ++c) + it++; + delcount--; + } + } + } + } +} + + +#if defined(__SUNPRO_CC) && defined(_RWSTD_VER) +# if !defined(SWIG_NO_STD_NOITERATOR_TRAITS_STL) +# define SWIG_STD_NOITERATOR_TRAITS_STL +# endif +#endif + +#if !defined(SWIG_STD_NOITERATOR_TRAITS_STL) +#include +#else +namespace std { + template + struct iterator_traits { + typedef ptrdiff_t difference_type; + typedef typename Iterator::value_type value_type; + }; + + template + struct iterator_traits<__reverse_bi_iterator > { + typedef Distance difference_type; + typedef T value_type; + }; + + template + struct iterator_traits { + typedef T value_type; + typedef ptrdiff_t difference_type; + }; + + template + inline typename iterator_traits<_InputIterator>::difference_type + distance(_InputIterator __first, _InputIterator __last) + { + typename iterator_traits<_InputIterator>::difference_type __n = 0; + while (__first != __last) { + ++__first; ++__n; + } + return __n; + } +} +#endif + + +namespace swig { + template + class SwigPyIterator_T : public SwigPyIterator + { + public: + typedef OutIterator out_iterator; + typedef typename std::iterator_traits::value_type value_type; + typedef SwigPyIterator_T self_type; + + SwigPyIterator_T(out_iterator curr, PyObject *seq) + : SwigPyIterator(seq), current(curr) + { + } + + const out_iterator& get_current() const + { + return current; + } + + + bool equal (const SwigPyIterator &iter) const + { + const self_type *iters = dynamic_cast(&iter); + if (iters) { + return (current == iters->get_current()); + } else { + throw std::invalid_argument("bad iterator type"); + } + } + + ptrdiff_t distance(const SwigPyIterator &iter) const + { + const self_type *iters = dynamic_cast(&iter); + if (iters) { + return std::distance(current, iters->get_current()); + } else { + throw std::invalid_argument("bad iterator type"); + } + } + + protected: + out_iterator current; + }; + + template + struct from_oper + { + typedef const ValueType& argument_type; + typedef PyObject *result_type; + result_type operator()(argument_type v) const + { + return swig::from(v); + } + }; + + template::value_type, + typename FromOper = from_oper > + class SwigPyIteratorOpen_T : public SwigPyIterator_T + { + public: + FromOper from; + typedef OutIterator out_iterator; + typedef ValueType value_type; + typedef SwigPyIterator_T base; + typedef SwigPyIteratorOpen_T self_type; + + SwigPyIteratorOpen_T(out_iterator curr, PyObject *seq) + : SwigPyIterator_T(curr, seq) + { + } + + PyObject *value() const { + return from(static_cast(*(base::current))); + } + + SwigPyIterator *copy() const + { + return new self_type(*this); + } + + SwigPyIterator *incr(size_t n = 1) + { + while (n--) { + ++base::current; + } + return this; + } + + SwigPyIterator *decr(size_t n = 1) + { + while (n--) { + --base::current; + } + return this; + } + }; + + template::value_type, + typename FromOper = from_oper > + class SwigPyIteratorClosed_T : public SwigPyIterator_T + { + public: + FromOper from; + typedef OutIterator out_iterator; + typedef ValueType value_type; + typedef SwigPyIterator_T base; + typedef SwigPyIteratorClosed_T self_type; + + SwigPyIteratorClosed_T(out_iterator curr, out_iterator first, out_iterator last, PyObject *seq) + : SwigPyIterator_T(curr, seq), begin(first), end(last) + { + } + + PyObject *value() const { + if (base::current == end) { + throw stop_iteration(); + } else { + return from(static_cast(*(base::current))); + } + } + + SwigPyIterator *copy() const + { + return new self_type(*this); + } + + SwigPyIterator *incr(size_t n = 1) + { + while (n--) { + if (base::current == end) { + throw stop_iteration(); + } else { + ++base::current; + } + } + return this; + } + + SwigPyIterator *decr(size_t n = 1) + { + while (n--) { + if (base::current == begin) { + throw stop_iteration(); + } else { + --base::current; + } + } + return this; + } + + private: + out_iterator begin; + out_iterator end; + }; + + template + inline SwigPyIterator* + make_output_iterator(const OutIter& current, const OutIter& begin,const OutIter& end, PyObject *seq = 0) + { + return new SwigPyIteratorClosed_T(current, begin, end, seq); + } + + template + inline SwigPyIterator* + make_output_iterator(const OutIter& current, PyObject *seq = 0) + { + return new SwigPyIteratorOpen_T(current, seq); + } + +} + + +namespace swig +{ + template + struct SwigPySequence_Ref + { + SwigPySequence_Ref(PyObject* seq, Py_ssize_t index) + : _seq(seq), _index(index) + { + } + + operator T () const + { + swig::SwigVar_PyObject item = PySequence_GetItem(_seq, _index); + try { + return swig::as(item, true); + } catch (std::exception& e) { + char msg[1024]; + sprintf(msg, "in sequence element %d ", (int)_index); + if (!PyErr_Occurred()) { + ::SWIG_Error(SWIG_TypeError, swig::type_name()); + } + SWIG_Python_AddErrorMsg(msg); + SWIG_Python_AddErrorMsg(e.what()); + throw; + } + } + + SwigPySequence_Ref& operator=(const T& v) + { + PySequence_SetItem(_seq, _index, swig::from(v)); + return *this; + } + + private: + PyObject* _seq; + Py_ssize_t _index; + }; + + template + struct SwigPySequence_ArrowProxy + { + SwigPySequence_ArrowProxy(const T& x): m_value(x) {} + const T* operator->() const { return &m_value; } + operator const T*() const { return &m_value; } + T m_value; + }; + + template + struct SwigPySequence_InputIterator + { + typedef SwigPySequence_InputIterator self; + + typedef std::random_access_iterator_tag iterator_category; + typedef Reference reference; + typedef T value_type; + typedef T* pointer; + typedef Py_ssize_t difference_type; + + SwigPySequence_InputIterator() + { + } + + SwigPySequence_InputIterator(PyObject* seq, Py_ssize_t index) + : _seq(seq), _index(index) + { + } + + reference operator*() const + { + return reference(_seq, _index); + } + + SwigPySequence_ArrowProxy + operator->() const { + return SwigPySequence_ArrowProxy(operator*()); + } + + bool operator==(const self& ri) const + { + return (_index == ri._index) && (_seq == ri._seq); + } + + bool operator!=(const self& ri) const + { + return !(operator==(ri)); + } + + self& operator ++ () + { + ++_index; + return *this; + } + + self& operator -- () + { + --_index; + return *this; + } + + self& operator += (difference_type n) + { + _index += n; + return *this; + } + + self operator +(difference_type n) const + { + return self(_seq, _index + n); + } + + self& operator -= (difference_type n) + { + _index -= n; + return *this; + } + + self operator -(difference_type n) const + { + return self(_seq, _index - n); + } + + difference_type operator - (const self& ri) const + { + return _index - ri._index; + } + + bool operator < (const self& ri) const + { + return _index < ri._index; + } + + reference + operator[](difference_type n) const + { + return reference(_seq, _index + n); + } + + private: + PyObject* _seq; + difference_type _index; + }; + + // STL container wrapper around a Python sequence + template + struct SwigPySequence_Cont + { + typedef SwigPySequence_Ref reference; + typedef const SwigPySequence_Ref const_reference; + typedef T value_type; + typedef T* pointer; + typedef Py_ssize_t difference_type; + typedef size_t size_type; + typedef const pointer const_pointer; + typedef SwigPySequence_InputIterator iterator; + typedef SwigPySequence_InputIterator const_iterator; + + SwigPySequence_Cont(PyObject* seq) : _seq(0) + { + if (!PySequence_Check(seq)) { + throw std::invalid_argument("a sequence is expected"); + } + _seq = seq; + Py_INCREF(_seq); + } + + ~SwigPySequence_Cont() + { + Py_XDECREF(_seq); + } + + size_type size() const + { + return static_cast(PySequence_Size(_seq)); + } + + bool empty() const + { + return size() == 0; + } + + iterator begin() + { + return iterator(_seq, 0); + } + + const_iterator begin() const + { + return const_iterator(_seq, 0); + } + + iterator end() + { + return iterator(_seq, size()); + } + + const_iterator end() const + { + return const_iterator(_seq, size()); + } + + reference operator[](difference_type n) + { + return reference(_seq, n); + } + + const_reference operator[](difference_type n) const + { + return const_reference(_seq, n); + } + + bool check(bool set_err = true) const + { + Py_ssize_t s = size(); + for (Py_ssize_t i = 0; i < s; ++i) { + swig::SwigVar_PyObject item = PySequence_GetItem(_seq, i); + if (!swig::check(item)) { + if (set_err) { + char msg[1024]; + sprintf(msg, "in sequence element %d", (int)i); + SWIG_Error(SWIG_RuntimeError, msg); + } + return false; + } + } + return true; + } + + private: + PyObject* _seq; + }; + +} + + + #define SWIG_From_double PyFloat_FromDouble + + +namespace swig { + template <> struct traits< double > { + typedef value_category category; + static const char* type_name() { return"double"; } + }; + template <> struct traits_asval< double > { + typedef double value_type; + static int asval(PyObject *obj, value_type *val) { + return SWIG_AsVal_double (obj, val); + } + }; + template <> struct traits_from< double > { + typedef double value_type; + static PyObject *from(const value_type& val) { + return SWIG_From_double (val); + } + }; +} + + +namespace swig { + template + inline void + assign(const SwigPySeq& swigpyseq, Seq* seq) { + // seq->assign(swigpyseq.begin(), swigpyseq.end()); // not used as not always implemented + typedef typename SwigPySeq::value_type value_type; + typename SwigPySeq::const_iterator it = swigpyseq.begin(); + for (;it != swigpyseq.end(); ++it) { + seq->insert(seq->end(),(value_type)(*it)); + } + } + + template + struct traits_asptr_stdseq { + typedef Seq sequence; + typedef T value_type; + + static int asptr(PyObject *obj, sequence **seq) { + if (obj == Py_None || SWIG_Python_GetSwigThis(obj)) { + sequence *p; + if (::SWIG_ConvertPtr(obj,(void**)&p, + swig::type_info(),0) == SWIG_OK) { + if (seq) *seq = p; + return SWIG_OLDOBJ; + } + } else if (PySequence_Check(obj)) { + try { + SwigPySequence_Cont swigpyseq(obj); + if (seq) { + sequence *pseq = new sequence(); + assign(swigpyseq, pseq); + *seq = pseq; + return SWIG_NEWOBJ; + } else { + return swigpyseq.check() ? SWIG_OK : SWIG_ERROR; + } + } catch (std::exception& e) { + if (seq) { + if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, e.what()); + } + } + return SWIG_ERROR; + } + } + return SWIG_ERROR; + } + }; + + template + struct traits_from_stdseq { + typedef Seq sequence; + typedef T value_type; + typedef typename Seq::size_type size_type; + typedef typename sequence::const_iterator const_iterator; + + static PyObject *from(const sequence& seq) { +#ifdef SWIG_PYTHON_EXTRA_NATIVE_CONTAINERS + swig_type_info *desc = swig::type_info(); + if (desc && desc->clientdata) { + return SWIG_NewPointerObj(new sequence(seq), desc, SWIG_POINTER_OWN); + } +#endif + size_type size = seq.size(); + if (size <= (size_type)INT_MAX) { + PyObject *obj = PyTuple_New((Py_ssize_t)size); + Py_ssize_t i = 0; + for (const_iterator it = seq.begin(); it != seq.end(); ++it, ++i) { + PyTuple_SetItem(obj,i,swig::from(*it)); + } + return obj; + } else { + PyErr_SetString(PyExc_OverflowError,"sequence size not valid in python"); + return NULL; + } + } + }; +} + + + namespace swig { + template + struct traits_asptr > { + static int asptr(PyObject *obj, std::vector **vec) { + return traits_asptr_stdseq >::asptr(obj, vec); + } + }; + + template + struct traits_from > { + static PyObject *from(const std::vector& vec) { + return traits_from_stdseq >::from(vec); + } + }; + } + + + namespace swig { + template <> struct traits > > { + typedef pointer_category category; + static const char* type_name() { + return "std::vector<" "double" "," "std::allocator< double >" " >"; + } + }; + } + +SWIGINTERN swig::SwigPyIterator *std_vector_Sl_double_Sg__iterator(std::vector< double > *self,PyObject **PYTHON_SELF){ + return swig::make_output_iterator(self->begin(), self->begin(), self->end(), *PYTHON_SELF); + } +SWIGINTERN bool std_vector_Sl_double_Sg____nonzero__(std::vector< double > const *self){ + return !(self->empty()); + } +SWIGINTERN bool std_vector_Sl_double_Sg____bool__(std::vector< double > const *self){ + return !(self->empty()); + } +SWIGINTERN std::vector< double >::size_type std_vector_Sl_double_Sg____len__(std::vector< double > const *self){ + return self->size(); + } + +SWIGINTERNINLINE PyObject* +SWIG_From_unsigned_SS_long (unsigned long value) +{ + return (value > LONG_MAX) ? + PyLong_FromUnsignedLong(value) : PyLong_FromLong(static_cast< long >(value)); +} + + +SWIGINTERNINLINE PyObject * +SWIG_From_size_t (size_t value) +{ + return SWIG_From_unsigned_SS_long (static_cast< unsigned long >(value)); +} + +SWIGINTERN std::vector< double,std::allocator< double > > *std_vector_Sl_double_Sg____getslice__(std::vector< double > *self,std::vector< double >::difference_type i,std::vector< double >::difference_type j){ + return swig::getslice(self, i, j, 1); + } +SWIGINTERN void std_vector_Sl_double_Sg____setslice____SWIG_0(std::vector< double > *self,std::vector< double >::difference_type i,std::vector< double >::difference_type j){ + swig::setslice(self, i, j, 1, std::vector< double,std::allocator< double > >()); + } +SWIGINTERN void std_vector_Sl_double_Sg____setslice____SWIG_1(std::vector< double > *self,std::vector< double >::difference_type i,std::vector< double >::difference_type j,std::vector< double,std::allocator< double > > const &v){ + swig::setslice(self, i, j, 1, v); + } +SWIGINTERN void std_vector_Sl_double_Sg____delslice__(std::vector< double > *self,std::vector< double >::difference_type i,std::vector< double >::difference_type j){ + swig::delslice(self, i, j, 1); + } +SWIGINTERN void std_vector_Sl_double_Sg____delitem____SWIG_0(std::vector< double > *self,std::vector< double >::difference_type i){ + swig::erase(self, swig::getpos(self, i)); + } +SWIGINTERN std::vector< double,std::allocator< double > > *std_vector_Sl_double_Sg____getitem____SWIG_0(std::vector< double > *self,PySliceObject *slice){ + Py_ssize_t i, j, step; + if( !PySlice_Check(slice) ) { + SWIG_Error(SWIG_TypeError, "Slice object expected."); + return NULL; + } + PySlice_GetIndices(SWIGPY_SLICE_ARG(slice), (Py_ssize_t)self->size(), &i, &j, &step); + std::vector< double,std::allocator< double > >::difference_type id = i; + std::vector< double,std::allocator< double > >::difference_type jd = j; + return swig::getslice(self, id, jd, step); + } +SWIGINTERN void std_vector_Sl_double_Sg____setitem____SWIG_0(std::vector< double > *self,PySliceObject *slice,std::vector< double,std::allocator< double > > const &v){ + Py_ssize_t i, j, step; + if( !PySlice_Check(slice) ) { + SWIG_Error(SWIG_TypeError, "Slice object expected."); + return; + } + PySlice_GetIndices(SWIGPY_SLICE_ARG(slice), (Py_ssize_t)self->size(), &i, &j, &step); + std::vector< double,std::allocator< double > >::difference_type id = i; + std::vector< double,std::allocator< double > >::difference_type jd = j; + swig::setslice(self, id, jd, step, v); + } +SWIGINTERN void std_vector_Sl_double_Sg____setitem____SWIG_1(std::vector< double > *self,PySliceObject *slice){ + Py_ssize_t i, j, step; + if( !PySlice_Check(slice) ) { + SWIG_Error(SWIG_TypeError, "Slice object expected."); + return; + } + PySlice_GetIndices(SWIGPY_SLICE_ARG(slice), (Py_ssize_t)self->size(), &i, &j, &step); + std::vector< double,std::allocator< double > >::difference_type id = i; + std::vector< double,std::allocator< double > >::difference_type jd = j; + swig::delslice(self, id, jd, step); + } +SWIGINTERN void std_vector_Sl_double_Sg____delitem____SWIG_1(std::vector< double > *self,PySliceObject *slice){ + Py_ssize_t i, j, step; + if( !PySlice_Check(slice) ) { + SWIG_Error(SWIG_TypeError, "Slice object expected."); + return; + } + PySlice_GetIndices(SWIGPY_SLICE_ARG(slice), (Py_ssize_t)self->size(), &i, &j, &step); + std::vector< double,std::allocator< double > >::difference_type id = i; + std::vector< double,std::allocator< double > >::difference_type jd = j; + swig::delslice(self, id, jd, step); + } +SWIGINTERN std::vector< double >::value_type const &std_vector_Sl_double_Sg____getitem____SWIG_1(std::vector< double > const *self,std::vector< double >::difference_type i){ + return *(swig::cgetpos(self, i)); + } +SWIGINTERN void std_vector_Sl_double_Sg____setitem____SWIG_2(std::vector< double > *self,std::vector< double >::difference_type i,std::vector< double >::value_type const &x){ + *(swig::getpos(self,i)) = x; + } +SWIGINTERN std::vector< double >::value_type std_vector_Sl_double_Sg__pop(std::vector< double > *self){ + if (self->size() == 0) + throw std::out_of_range("pop from empty container"); + std::vector< double,std::allocator< double > >::value_type x = self->back(); + self->pop_back(); + return x; + } +SWIGINTERN void std_vector_Sl_double_Sg__append(std::vector< double > *self,std::vector< double >::value_type const &x){ + self->push_back(x); + } +SWIGINTERN std::vector< double >::iterator std_vector_Sl_double_Sg__erase__SWIG_0(std::vector< double > *self,std::vector< double >::iterator pos){ return self->erase(pos); } +SWIGINTERN std::vector< double >::iterator std_vector_Sl_double_Sg__erase__SWIG_1(std::vector< double > *self,std::vector< double >::iterator first,std::vector< double >::iterator last){ return self->erase(first, last); } +SWIGINTERN std::vector< double >::iterator std_vector_Sl_double_Sg__insert__SWIG_0(std::vector< double > *self,std::vector< double >::iterator pos,std::vector< double >::value_type const &x){ return self->insert(pos, x); } +SWIGINTERN void std_vector_Sl_double_Sg__insert__SWIG_1(std::vector< double > *self,std::vector< double >::iterator pos,std::vector< double >::size_type n,std::vector< double >::value_type const &x){ self->insert(pos, n, x); } + +#define SWIG_FILE_WITH_INIT +#include +#include +#include +#include + +#include "polyiou.h" + +#ifdef __cplusplus +extern "C" { +#endif +SWIGINTERN PyObject *_wrap_delete_SwigPyIterator(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"O:delete_SwigPyIterator",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, SWIG_POINTER_DISOWN | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_SwigPyIterator" "', argument " "1"" of type '" "swig::SwigPyIterator *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + delete arg1; + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator_value(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + PyObject *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"O:SwigPyIterator_value",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator_value" "', argument " "1"" of type '" "swig::SwigPyIterator const *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + try { + result = (PyObject *)((swig::SwigPyIterator const *)arg1)->value(); + } + catch(swig::stop_iteration &_e) { + { + (void)_e; + SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); + SWIG_fail; + } + } + + resultobj = result; + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator_incr__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + size_t arg2 ; + void *argp1 = 0 ; + int res1 = 0 ; + size_t val2 ; + int ecode2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + swig::SwigPyIterator *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:SwigPyIterator_incr",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator_incr" "', argument " "1"" of type '" "swig::SwigPyIterator *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + ecode2 = SWIG_AsVal_size_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SwigPyIterator_incr" "', argument " "2"" of type '" "size_t""'"); + } + arg2 = static_cast< size_t >(val2); + try { + result = (swig::SwigPyIterator *)(arg1)->incr(arg2); + } + catch(swig::stop_iteration &_e) { + { + (void)_e; + SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); + SWIG_fail; + } + } + + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator_incr__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + swig::SwigPyIterator *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"O:SwigPyIterator_incr",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator_incr" "', argument " "1"" of type '" "swig::SwigPyIterator *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + try { + result = (swig::SwigPyIterator *)(arg1)->incr(); + } + catch(swig::stop_iteration &_e) { + { + (void)_e; + SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); + SWIG_fail; + } + } + + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator_incr(PyObject *self, PyObject *args) { + Py_ssize_t argc; + PyObject *argv[3] = { + 0 + }; + Py_ssize_t ii; + + if (!PyTuple_Check(args)) SWIG_fail; + argc = args ? PyObject_Length(args) : 0; + for (ii = 0; (ii < 2) && (ii < argc); ii++) { + argv[ii] = PyTuple_GET_ITEM(args,ii); + } + if (argc == 1) { + int _v; + void *vptr = 0; + int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_swig__SwigPyIterator, 0); + _v = SWIG_CheckState(res); + if (_v) { + return _wrap_SwigPyIterator_incr__SWIG_1(self, args); + } + } + if (argc == 2) { + int _v; + void *vptr = 0; + int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_swig__SwigPyIterator, 0); + _v = SWIG_CheckState(res); + if (_v) { + { + int res = SWIG_AsVal_size_t(argv[1], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + return _wrap_SwigPyIterator_incr__SWIG_0(self, args); + } + } + } + +fail: + SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number or type of arguments for overloaded function 'SwigPyIterator_incr'.\n" + " Possible C/C++ prototypes are:\n" + " swig::SwigPyIterator::incr(size_t)\n" + " swig::SwigPyIterator::incr()\n"); + return 0; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator_decr__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + size_t arg2 ; + void *argp1 = 0 ; + int res1 = 0 ; + size_t val2 ; + int ecode2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + swig::SwigPyIterator *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:SwigPyIterator_decr",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator_decr" "', argument " "1"" of type '" "swig::SwigPyIterator *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + ecode2 = SWIG_AsVal_size_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SwigPyIterator_decr" "', argument " "2"" of type '" "size_t""'"); + } + arg2 = static_cast< size_t >(val2); + try { + result = (swig::SwigPyIterator *)(arg1)->decr(arg2); + } + catch(swig::stop_iteration &_e) { + { + (void)_e; + SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); + SWIG_fail; + } + } + + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator_decr__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + swig::SwigPyIterator *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"O:SwigPyIterator_decr",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator_decr" "', argument " "1"" of type '" "swig::SwigPyIterator *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + try { + result = (swig::SwigPyIterator *)(arg1)->decr(); + } + catch(swig::stop_iteration &_e) { + { + (void)_e; + SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); + SWIG_fail; + } + } + + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator_decr(PyObject *self, PyObject *args) { + Py_ssize_t argc; + PyObject *argv[3] = { + 0 + }; + Py_ssize_t ii; + + if (!PyTuple_Check(args)) SWIG_fail; + argc = args ? PyObject_Length(args) : 0; + for (ii = 0; (ii < 2) && (ii < argc); ii++) { + argv[ii] = PyTuple_GET_ITEM(args,ii); + } + if (argc == 1) { + int _v; + void *vptr = 0; + int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_swig__SwigPyIterator, 0); + _v = SWIG_CheckState(res); + if (_v) { + return _wrap_SwigPyIterator_decr__SWIG_1(self, args); + } + } + if (argc == 2) { + int _v; + void *vptr = 0; + int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_swig__SwigPyIterator, 0); + _v = SWIG_CheckState(res); + if (_v) { + { + int res = SWIG_AsVal_size_t(argv[1], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + return _wrap_SwigPyIterator_decr__SWIG_0(self, args); + } + } + } + +fail: + SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number or type of arguments for overloaded function 'SwigPyIterator_decr'.\n" + " Possible C/C++ prototypes are:\n" + " swig::SwigPyIterator::decr(size_t)\n" + " swig::SwigPyIterator::decr()\n"); + return 0; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator_distance(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + swig::SwigPyIterator *arg2 = 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + void *argp2 = 0 ; + int res2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + ptrdiff_t result; + + if (!PyArg_ParseTuple(args,(char *)"OO:SwigPyIterator_distance",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator_distance" "', argument " "1"" of type '" "swig::SwigPyIterator const *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + res2 = SWIG_ConvertPtr(obj1, &argp2, SWIGTYPE_p_swig__SwigPyIterator, 0 | 0); + if (!SWIG_IsOK(res2)) { + SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "SwigPyIterator_distance" "', argument " "2"" of type '" "swig::SwigPyIterator const &""'"); + } + if (!argp2) { + SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "SwigPyIterator_distance" "', argument " "2"" of type '" "swig::SwigPyIterator const &""'"); + } + arg2 = reinterpret_cast< swig::SwigPyIterator * >(argp2); + try { + result = ((swig::SwigPyIterator const *)arg1)->distance((swig::SwigPyIterator const &)*arg2); + } + catch(std::invalid_argument &_e) { + SWIG_Python_Raise(SWIG_NewPointerObj((new std::invalid_argument(static_cast< const std::invalid_argument& >(_e))),SWIGTYPE_p_std__invalid_argument,SWIG_POINTER_OWN), "std::invalid_argument", SWIGTYPE_p_std__invalid_argument); SWIG_fail; + } + + resultobj = SWIG_From_ptrdiff_t(static_cast< ptrdiff_t >(result)); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator_equal(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + swig::SwigPyIterator *arg2 = 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + void *argp2 = 0 ; + int res2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + bool result; + + if (!PyArg_ParseTuple(args,(char *)"OO:SwigPyIterator_equal",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator_equal" "', argument " "1"" of type '" "swig::SwigPyIterator const *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + res2 = SWIG_ConvertPtr(obj1, &argp2, SWIGTYPE_p_swig__SwigPyIterator, 0 | 0); + if (!SWIG_IsOK(res2)) { + SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "SwigPyIterator_equal" "', argument " "2"" of type '" "swig::SwigPyIterator const &""'"); + } + if (!argp2) { + SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "SwigPyIterator_equal" "', argument " "2"" of type '" "swig::SwigPyIterator const &""'"); + } + arg2 = reinterpret_cast< swig::SwigPyIterator * >(argp2); + try { + result = (bool)((swig::SwigPyIterator const *)arg1)->equal((swig::SwigPyIterator const &)*arg2); + } + catch(std::invalid_argument &_e) { + SWIG_Python_Raise(SWIG_NewPointerObj((new std::invalid_argument(static_cast< const std::invalid_argument& >(_e))),SWIGTYPE_p_std__invalid_argument,SWIG_POINTER_OWN), "std::invalid_argument", SWIGTYPE_p_std__invalid_argument); SWIG_fail; + } + + resultobj = SWIG_From_bool(static_cast< bool >(result)); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator_copy(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + swig::SwigPyIterator *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"O:SwigPyIterator_copy",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator_copy" "', argument " "1"" of type '" "swig::SwigPyIterator const *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + result = (swig::SwigPyIterator *)((swig::SwigPyIterator const *)arg1)->copy(); + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__SwigPyIterator, SWIG_POINTER_OWN | 0 ); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator_next(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + PyObject *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"O:SwigPyIterator_next",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator_next" "', argument " "1"" of type '" "swig::SwigPyIterator *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + try { + result = (PyObject *)(arg1)->next(); + } + catch(swig::stop_iteration &_e) { + { + (void)_e; + SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); + SWIG_fail; + } + } + + resultobj = result; + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator___next__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + PyObject *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"O:SwigPyIterator___next__",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator___next__" "', argument " "1"" of type '" "swig::SwigPyIterator *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + try { + result = (PyObject *)(arg1)->__next__(); + } + catch(swig::stop_iteration &_e) { + { + (void)_e; + SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); + SWIG_fail; + } + } + + resultobj = result; + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator_previous(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + PyObject *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"O:SwigPyIterator_previous",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator_previous" "', argument " "1"" of type '" "swig::SwigPyIterator *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + try { + result = (PyObject *)(arg1)->previous(); + } + catch(swig::stop_iteration &_e) { + { + (void)_e; + SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); + SWIG_fail; + } + } + + resultobj = result; + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator_advance(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + ptrdiff_t arg2 ; + void *argp1 = 0 ; + int res1 = 0 ; + ptrdiff_t val2 ; + int ecode2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + swig::SwigPyIterator *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:SwigPyIterator_advance",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator_advance" "', argument " "1"" of type '" "swig::SwigPyIterator *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SwigPyIterator_advance" "', argument " "2"" of type '" "ptrdiff_t""'"); + } + arg2 = static_cast< ptrdiff_t >(val2); + try { + result = (swig::SwigPyIterator *)(arg1)->advance(arg2); + } + catch(swig::stop_iteration &_e) { + { + (void)_e; + SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); + SWIG_fail; + } + } + + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator___eq__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + swig::SwigPyIterator *arg2 = 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + void *argp2 = 0 ; + int res2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + bool result; + + if (!PyArg_ParseTuple(args,(char *)"OO:SwigPyIterator___eq__",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator___eq__" "', argument " "1"" of type '" "swig::SwigPyIterator const *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + res2 = SWIG_ConvertPtr(obj1, &argp2, SWIGTYPE_p_swig__SwigPyIterator, 0 | 0); + if (!SWIG_IsOK(res2)) { + SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "SwigPyIterator___eq__" "', argument " "2"" of type '" "swig::SwigPyIterator const &""'"); + } + if (!argp2) { + SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "SwigPyIterator___eq__" "', argument " "2"" of type '" "swig::SwigPyIterator const &""'"); + } + arg2 = reinterpret_cast< swig::SwigPyIterator * >(argp2); + result = (bool)((swig::SwigPyIterator const *)arg1)->operator ==((swig::SwigPyIterator const &)*arg2); + resultobj = SWIG_From_bool(static_cast< bool >(result)); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator___ne__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + swig::SwigPyIterator *arg2 = 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + void *argp2 = 0 ; + int res2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + bool result; + + if (!PyArg_ParseTuple(args,(char *)"OO:SwigPyIterator___ne__",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator___ne__" "', argument " "1"" of type '" "swig::SwigPyIterator const *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + res2 = SWIG_ConvertPtr(obj1, &argp2, SWIGTYPE_p_swig__SwigPyIterator, 0 | 0); + if (!SWIG_IsOK(res2)) { + SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "SwigPyIterator___ne__" "', argument " "2"" of type '" "swig::SwigPyIterator const &""'"); + } + if (!argp2) { + SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "SwigPyIterator___ne__" "', argument " "2"" of type '" "swig::SwigPyIterator const &""'"); + } + arg2 = reinterpret_cast< swig::SwigPyIterator * >(argp2); + result = (bool)((swig::SwigPyIterator const *)arg1)->operator !=((swig::SwigPyIterator const &)*arg2); + resultobj = SWIG_From_bool(static_cast< bool >(result)); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator___iadd__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + ptrdiff_t arg2 ; + void *argp1 = 0 ; + int res1 = 0 ; + ptrdiff_t val2 ; + int ecode2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + swig::SwigPyIterator *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:SwigPyIterator___iadd__",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, SWIG_POINTER_DISOWN | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator___iadd__" "', argument " "1"" of type '" "swig::SwigPyIterator *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SwigPyIterator___iadd__" "', argument " "2"" of type '" "ptrdiff_t""'"); + } + arg2 = static_cast< ptrdiff_t >(val2); + try { + result = (swig::SwigPyIterator *) &(arg1)->operator +=(arg2); + } + catch(swig::stop_iteration &_e) { + { + (void)_e; + SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); + SWIG_fail; + } + } + + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__SwigPyIterator, SWIG_POINTER_OWN | 0 ); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator___isub__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + ptrdiff_t arg2 ; + void *argp1 = 0 ; + int res1 = 0 ; + ptrdiff_t val2 ; + int ecode2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + swig::SwigPyIterator *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:SwigPyIterator___isub__",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, SWIG_POINTER_DISOWN | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator___isub__" "', argument " "1"" of type '" "swig::SwigPyIterator *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SwigPyIterator___isub__" "', argument " "2"" of type '" "ptrdiff_t""'"); + } + arg2 = static_cast< ptrdiff_t >(val2); + try { + result = (swig::SwigPyIterator *) &(arg1)->operator -=(arg2); + } + catch(swig::stop_iteration &_e) { + { + (void)_e; + SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); + SWIG_fail; + } + } + + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__SwigPyIterator, SWIG_POINTER_OWN | 0 ); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator___add__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + ptrdiff_t arg2 ; + void *argp1 = 0 ; + int res1 = 0 ; + ptrdiff_t val2 ; + int ecode2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + swig::SwigPyIterator *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:SwigPyIterator___add__",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator___add__" "', argument " "1"" of type '" "swig::SwigPyIterator const *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SwigPyIterator___add__" "', argument " "2"" of type '" "ptrdiff_t""'"); + } + arg2 = static_cast< ptrdiff_t >(val2); + try { + result = (swig::SwigPyIterator *)((swig::SwigPyIterator const *)arg1)->operator +(arg2); + } + catch(swig::stop_iteration &_e) { + { + (void)_e; + SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); + SWIG_fail; + } + } + + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__SwigPyIterator, SWIG_POINTER_OWN | 0 ); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator___sub____SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + ptrdiff_t arg2 ; + void *argp1 = 0 ; + int res1 = 0 ; + ptrdiff_t val2 ; + int ecode2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + swig::SwigPyIterator *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:SwigPyIterator___sub__",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator___sub__" "', argument " "1"" of type '" "swig::SwigPyIterator const *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "SwigPyIterator___sub__" "', argument " "2"" of type '" "ptrdiff_t""'"); + } + arg2 = static_cast< ptrdiff_t >(val2); + try { + result = (swig::SwigPyIterator *)((swig::SwigPyIterator const *)arg1)->operator -(arg2); + } + catch(swig::stop_iteration &_e) { + { + (void)_e; + SWIG_SetErrorObj(PyExc_StopIteration, SWIG_Py_Void()); + SWIG_fail; + } + } + + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__SwigPyIterator, SWIG_POINTER_OWN | 0 ); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator___sub____SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + swig::SwigPyIterator *arg1 = (swig::SwigPyIterator *) 0 ; + swig::SwigPyIterator *arg2 = 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + void *argp2 = 0 ; + int res2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + ptrdiff_t result; + + if (!PyArg_ParseTuple(args,(char *)"OO:SwigPyIterator___sub__",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_swig__SwigPyIterator, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "SwigPyIterator___sub__" "', argument " "1"" of type '" "swig::SwigPyIterator const *""'"); + } + arg1 = reinterpret_cast< swig::SwigPyIterator * >(argp1); + res2 = SWIG_ConvertPtr(obj1, &argp2, SWIGTYPE_p_swig__SwigPyIterator, 0 | 0); + if (!SWIG_IsOK(res2)) { + SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "SwigPyIterator___sub__" "', argument " "2"" of type '" "swig::SwigPyIterator const &""'"); + } + if (!argp2) { + SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "SwigPyIterator___sub__" "', argument " "2"" of type '" "swig::SwigPyIterator const &""'"); + } + arg2 = reinterpret_cast< swig::SwigPyIterator * >(argp2); + result = ((swig::SwigPyIterator const *)arg1)->operator -((swig::SwigPyIterator const &)*arg2); + resultobj = SWIG_From_ptrdiff_t(static_cast< ptrdiff_t >(result)); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_SwigPyIterator___sub__(PyObject *self, PyObject *args) { + Py_ssize_t argc; + PyObject *argv[3] = { + 0 + }; + Py_ssize_t ii; + + if (!PyTuple_Check(args)) SWIG_fail; + argc = args ? PyObject_Length(args) : 0; + for (ii = 0; (ii < 2) && (ii < argc); ii++) { + argv[ii] = PyTuple_GET_ITEM(args,ii); + } + if (argc == 2) { + int _v; + void *vptr = 0; + int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_swig__SwigPyIterator, 0); + _v = SWIG_CheckState(res); + if (_v) { + int res = SWIG_ConvertPtr(argv[1], 0, SWIGTYPE_p_swig__SwigPyIterator, 0); + _v = SWIG_CheckState(res); + if (_v) { + return _wrap_SwigPyIterator___sub____SWIG_1(self, args); + } + } + } + if (argc == 2) { + int _v; + void *vptr = 0; + int res = SWIG_ConvertPtr(argv[0], &vptr, SWIGTYPE_p_swig__SwigPyIterator, 0); + _v = SWIG_CheckState(res); + if (_v) { + { + int res = SWIG_AsVal_ptrdiff_t(argv[1], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + return _wrap_SwigPyIterator___sub____SWIG_0(self, args); + } + } + } + +fail: + Py_INCREF(Py_NotImplemented); + return Py_NotImplemented; +} + + +SWIGINTERN PyObject *SwigPyIterator_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *obj; + if (!PyArg_ParseTuple(args,(char*)"O:swigregister", &obj)) return NULL; + SWIG_TypeNewClientData(SWIGTYPE_p_swig__SwigPyIterator, SWIG_NewClientData(obj)); + return SWIG_Py_Void(); +} + +SWIGINTERN PyObject *_wrap_VectorDouble_iterator(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + PyObject **arg2 = (PyObject **) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + swig::SwigPyIterator *result = 0 ; + + arg2 = &obj0; + if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_iterator",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_iterator" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + result = (swig::SwigPyIterator *)std_vector_Sl_double_Sg__iterator(arg1,arg2); + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_swig__SwigPyIterator, SWIG_POINTER_OWN | 0 ); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___nonzero__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + bool result; + + if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble___nonzero__",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___nonzero__" "', argument " "1"" of type '" "std::vector< double > const *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + result = (bool)std_vector_Sl_double_Sg____nonzero__((std::vector< double > const *)arg1); + resultobj = SWIG_From_bool(static_cast< bool >(result)); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___bool__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + bool result; + + if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble___bool__",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___bool__" "', argument " "1"" of type '" "std::vector< double > const *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + result = (bool)std_vector_Sl_double_Sg____bool__((std::vector< double > const *)arg1); + resultobj = SWIG_From_bool(static_cast< bool >(result)); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___len__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + std::vector< double >::size_type result; + + if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble___len__",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___len__" "', argument " "1"" of type '" "std::vector< double > const *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + result = std_vector_Sl_double_Sg____len__((std::vector< double > const *)arg1); + resultobj = SWIG_From_size_t(static_cast< size_t >(result)); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___getslice__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double >::difference_type arg2 ; + std::vector< double >::difference_type arg3 ; + void *argp1 = 0 ; + int res1 = 0 ; + ptrdiff_t val2 ; + int ecode2 = 0 ; + ptrdiff_t val3 ; + int ecode3 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + PyObject * obj2 = 0 ; + std::vector< double,std::allocator< double > > *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OOO:VectorDouble___getslice__",&obj0,&obj1,&obj2)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___getslice__" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble___getslice__" "', argument " "2"" of type '" "std::vector< double >::difference_type""'"); + } + arg2 = static_cast< std::vector< double >::difference_type >(val2); + ecode3 = SWIG_AsVal_ptrdiff_t(obj2, &val3); + if (!SWIG_IsOK(ecode3)) { + SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "VectorDouble___getslice__" "', argument " "3"" of type '" "std::vector< double >::difference_type""'"); + } + arg3 = static_cast< std::vector< double >::difference_type >(val3); + try { + result = (std::vector< double,std::allocator< double > > *)std_vector_Sl_double_Sg____getslice__(arg1,arg2,arg3); + } + catch(std::out_of_range &_e) { + SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); + } + catch(std::invalid_argument &_e) { + SWIG_exception_fail(SWIG_ValueError, (&_e)->what()); + } + + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, SWIG_POINTER_OWN | 0 ); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___setslice____SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double >::difference_type arg2 ; + std::vector< double >::difference_type arg3 ; + void *argp1 = 0 ; + int res1 = 0 ; + ptrdiff_t val2 ; + int ecode2 = 0 ; + ptrdiff_t val3 ; + int ecode3 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + PyObject * obj2 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OOO:VectorDouble___setslice__",&obj0,&obj1,&obj2)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___setslice__" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble___setslice__" "', argument " "2"" of type '" "std::vector< double >::difference_type""'"); + } + arg2 = static_cast< std::vector< double >::difference_type >(val2); + ecode3 = SWIG_AsVal_ptrdiff_t(obj2, &val3); + if (!SWIG_IsOK(ecode3)) { + SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "VectorDouble___setslice__" "', argument " "3"" of type '" "std::vector< double >::difference_type""'"); + } + arg3 = static_cast< std::vector< double >::difference_type >(val3); + try { + std_vector_Sl_double_Sg____setslice____SWIG_0(arg1,arg2,arg3); + } + catch(std::out_of_range &_e) { + SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); + } + catch(std::invalid_argument &_e) { + SWIG_exception_fail(SWIG_ValueError, (&_e)->what()); + } + + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___setslice____SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double >::difference_type arg2 ; + std::vector< double >::difference_type arg3 ; + std::vector< double,std::allocator< double > > *arg4 = 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + ptrdiff_t val2 ; + int ecode2 = 0 ; + ptrdiff_t val3 ; + int ecode3 = 0 ; + int res4 = SWIG_OLDOBJ ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + PyObject * obj2 = 0 ; + PyObject * obj3 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OOOO:VectorDouble___setslice__",&obj0,&obj1,&obj2,&obj3)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___setslice__" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble___setslice__" "', argument " "2"" of type '" "std::vector< double >::difference_type""'"); + } + arg2 = static_cast< std::vector< double >::difference_type >(val2); + ecode3 = SWIG_AsVal_ptrdiff_t(obj2, &val3); + if (!SWIG_IsOK(ecode3)) { + SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "VectorDouble___setslice__" "', argument " "3"" of type '" "std::vector< double >::difference_type""'"); + } + arg3 = static_cast< std::vector< double >::difference_type >(val3); + { + std::vector< double,std::allocator< double > > *ptr = (std::vector< double,std::allocator< double > > *)0; + res4 = swig::asptr(obj3, &ptr); + if (!SWIG_IsOK(res4)) { + SWIG_exception_fail(SWIG_ArgError(res4), "in method '" "VectorDouble___setslice__" "', argument " "4"" of type '" "std::vector< double,std::allocator< double > > const &""'"); + } + if (!ptr) { + SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "VectorDouble___setslice__" "', argument " "4"" of type '" "std::vector< double,std::allocator< double > > const &""'"); + } + arg4 = ptr; + } + try { + std_vector_Sl_double_Sg____setslice____SWIG_1(arg1,arg2,arg3,(std::vector< double,std::allocator< double > > const &)*arg4); + } + catch(std::out_of_range &_e) { + SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); + } + catch(std::invalid_argument &_e) { + SWIG_exception_fail(SWIG_ValueError, (&_e)->what()); + } + + resultobj = SWIG_Py_Void(); + if (SWIG_IsNewObj(res4)) delete arg4; + return resultobj; +fail: + if (SWIG_IsNewObj(res4)) delete arg4; + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___setslice__(PyObject *self, PyObject *args) { + Py_ssize_t argc; + PyObject *argv[5] = { + 0 + }; + Py_ssize_t ii; + + if (!PyTuple_Check(args)) SWIG_fail; + argc = args ? PyObject_Length(args) : 0; + for (ii = 0; (ii < 4) && (ii < argc); ii++) { + argv[ii] = PyTuple_GET_ITEM(args,ii); + } + if (argc == 3) { + int _v; + int res = swig::asptr(argv[0], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + { + int res = SWIG_AsVal_ptrdiff_t(argv[1], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + { + int res = SWIG_AsVal_ptrdiff_t(argv[2], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + return _wrap_VectorDouble___setslice____SWIG_0(self, args); + } + } + } + } + if (argc == 4) { + int _v; + int res = swig::asptr(argv[0], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + { + int res = SWIG_AsVal_ptrdiff_t(argv[1], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + { + int res = SWIG_AsVal_ptrdiff_t(argv[2], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + int res = swig::asptr(argv[3], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + return _wrap_VectorDouble___setslice____SWIG_1(self, args); + } + } + } + } + } + +fail: + SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number or type of arguments for overloaded function 'VectorDouble___setslice__'.\n" + " Possible C/C++ prototypes are:\n" + " std::vector< double >::__setslice__(std::vector< double >::difference_type,std::vector< double >::difference_type)\n" + " std::vector< double >::__setslice__(std::vector< double >::difference_type,std::vector< double >::difference_type,std::vector< double,std::allocator< double > > const &)\n"); + return 0; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___delslice__(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double >::difference_type arg2 ; + std::vector< double >::difference_type arg3 ; + void *argp1 = 0 ; + int res1 = 0 ; + ptrdiff_t val2 ; + int ecode2 = 0 ; + ptrdiff_t val3 ; + int ecode3 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + PyObject * obj2 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OOO:VectorDouble___delslice__",&obj0,&obj1,&obj2)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___delslice__" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble___delslice__" "', argument " "2"" of type '" "std::vector< double >::difference_type""'"); + } + arg2 = static_cast< std::vector< double >::difference_type >(val2); + ecode3 = SWIG_AsVal_ptrdiff_t(obj2, &val3); + if (!SWIG_IsOK(ecode3)) { + SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "VectorDouble___delslice__" "', argument " "3"" of type '" "std::vector< double >::difference_type""'"); + } + arg3 = static_cast< std::vector< double >::difference_type >(val3); + try { + std_vector_Sl_double_Sg____delslice__(arg1,arg2,arg3); + } + catch(std::out_of_range &_e) { + SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); + } + catch(std::invalid_argument &_e) { + SWIG_exception_fail(SWIG_ValueError, (&_e)->what()); + } + + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___delitem____SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double >::difference_type arg2 ; + void *argp1 = 0 ; + int res1 = 0 ; + ptrdiff_t val2 ; + int ecode2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble___delitem__",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___delitem__" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble___delitem__" "', argument " "2"" of type '" "std::vector< double >::difference_type""'"); + } + arg2 = static_cast< std::vector< double >::difference_type >(val2); + try { + std_vector_Sl_double_Sg____delitem____SWIG_0(arg1,arg2); + } + catch(std::out_of_range &_e) { + SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); + } + catch(std::invalid_argument &_e) { + SWIG_exception_fail(SWIG_ValueError, (&_e)->what()); + } + + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___getitem____SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + PySliceObject *arg2 = (PySliceObject *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + std::vector< double,std::allocator< double > > *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble___getitem__",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___getitem__" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + { + if (!PySlice_Check(obj1)) { + SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble___getitem__" "', argument " "2"" of type '" "PySliceObject *""'"); + } + arg2 = (PySliceObject *) obj1; + } + try { + result = (std::vector< double,std::allocator< double > > *)std_vector_Sl_double_Sg____getitem____SWIG_0(arg1,arg2); + } + catch(std::out_of_range &_e) { + SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); + } + catch(std::invalid_argument &_e) { + SWIG_exception_fail(SWIG_ValueError, (&_e)->what()); + } + + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, SWIG_POINTER_OWN | 0 ); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___setitem____SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + PySliceObject *arg2 = (PySliceObject *) 0 ; + std::vector< double,std::allocator< double > > *arg3 = 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + int res3 = SWIG_OLDOBJ ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + PyObject * obj2 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OOO:VectorDouble___setitem__",&obj0,&obj1,&obj2)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___setitem__" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + { + if (!PySlice_Check(obj1)) { + SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble___setitem__" "', argument " "2"" of type '" "PySliceObject *""'"); + } + arg2 = (PySliceObject *) obj1; + } + { + std::vector< double,std::allocator< double > > *ptr = (std::vector< double,std::allocator< double > > *)0; + res3 = swig::asptr(obj2, &ptr); + if (!SWIG_IsOK(res3)) { + SWIG_exception_fail(SWIG_ArgError(res3), "in method '" "VectorDouble___setitem__" "', argument " "3"" of type '" "std::vector< double,std::allocator< double > > const &""'"); + } + if (!ptr) { + SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "VectorDouble___setitem__" "', argument " "3"" of type '" "std::vector< double,std::allocator< double > > const &""'"); + } + arg3 = ptr; + } + try { + std_vector_Sl_double_Sg____setitem____SWIG_0(arg1,arg2,(std::vector< double,std::allocator< double > > const &)*arg3); + } + catch(std::out_of_range &_e) { + SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); + } + catch(std::invalid_argument &_e) { + SWIG_exception_fail(SWIG_ValueError, (&_e)->what()); + } + + resultobj = SWIG_Py_Void(); + if (SWIG_IsNewObj(res3)) delete arg3; + return resultobj; +fail: + if (SWIG_IsNewObj(res3)) delete arg3; + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___setitem____SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + PySliceObject *arg2 = (PySliceObject *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble___setitem__",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___setitem__" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + { + if (!PySlice_Check(obj1)) { + SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble___setitem__" "', argument " "2"" of type '" "PySliceObject *""'"); + } + arg2 = (PySliceObject *) obj1; + } + try { + std_vector_Sl_double_Sg____setitem____SWIG_1(arg1,arg2); + } + catch(std::out_of_range &_e) { + SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); + } + catch(std::invalid_argument &_e) { + SWIG_exception_fail(SWIG_ValueError, (&_e)->what()); + } + + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___delitem____SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + PySliceObject *arg2 = (PySliceObject *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble___delitem__",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___delitem__" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + { + if (!PySlice_Check(obj1)) { + SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble___delitem__" "', argument " "2"" of type '" "PySliceObject *""'"); + } + arg2 = (PySliceObject *) obj1; + } + try { + std_vector_Sl_double_Sg____delitem____SWIG_1(arg1,arg2); + } + catch(std::out_of_range &_e) { + SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); + } + catch(std::invalid_argument &_e) { + SWIG_exception_fail(SWIG_ValueError, (&_e)->what()); + } + + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___delitem__(PyObject *self, PyObject *args) { + Py_ssize_t argc; + PyObject *argv[3] = { + 0 + }; + Py_ssize_t ii; + + if (!PyTuple_Check(args)) SWIG_fail; + argc = args ? PyObject_Length(args) : 0; + for (ii = 0; (ii < 2) && (ii < argc); ii++) { + argv[ii] = PyTuple_GET_ITEM(args,ii); + } + if (argc == 2) { + int _v; + int res = swig::asptr(argv[0], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + { + _v = PySlice_Check(argv[1]); + } + if (_v) { + return _wrap_VectorDouble___delitem____SWIG_1(self, args); + } + } + } + if (argc == 2) { + int _v; + int res = swig::asptr(argv[0], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + { + int res = SWIG_AsVal_ptrdiff_t(argv[1], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + return _wrap_VectorDouble___delitem____SWIG_0(self, args); + } + } + } + +fail: + SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number or type of arguments for overloaded function 'VectorDouble___delitem__'.\n" + " Possible C/C++ prototypes are:\n" + " std::vector< double >::__delitem__(std::vector< double >::difference_type)\n" + " std::vector< double >::__delitem__(PySliceObject *)\n"); + return 0; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___getitem____SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double >::difference_type arg2 ; + void *argp1 = 0 ; + int res1 = 0 ; + ptrdiff_t val2 ; + int ecode2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + std::vector< double >::value_type *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble___getitem__",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___getitem__" "', argument " "1"" of type '" "std::vector< double > const *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble___getitem__" "', argument " "2"" of type '" "std::vector< double >::difference_type""'"); + } + arg2 = static_cast< std::vector< double >::difference_type >(val2); + try { + result = (std::vector< double >::value_type *) &std_vector_Sl_double_Sg____getitem____SWIG_1((std::vector< double > const *)arg1,arg2); + } + catch(std::out_of_range &_e) { + SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); + } + + resultobj = SWIG_From_double(static_cast< double >(*result)); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___getitem__(PyObject *self, PyObject *args) { + Py_ssize_t argc; + PyObject *argv[3] = { + 0 + }; + Py_ssize_t ii; + + if (!PyTuple_Check(args)) SWIG_fail; + argc = args ? PyObject_Length(args) : 0; + for (ii = 0; (ii < 2) && (ii < argc); ii++) { + argv[ii] = PyTuple_GET_ITEM(args,ii); + } + if (argc == 2) { + int _v; + int res = swig::asptr(argv[0], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + { + _v = PySlice_Check(argv[1]); + } + if (_v) { + return _wrap_VectorDouble___getitem____SWIG_0(self, args); + } + } + } + if (argc == 2) { + int _v; + int res = swig::asptr(argv[0], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + { + int res = SWIG_AsVal_ptrdiff_t(argv[1], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + return _wrap_VectorDouble___getitem____SWIG_1(self, args); + } + } + } + +fail: + SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number or type of arguments for overloaded function 'VectorDouble___getitem__'.\n" + " Possible C/C++ prototypes are:\n" + " std::vector< double >::__getitem__(PySliceObject *)\n" + " std::vector< double >::__getitem__(std::vector< double >::difference_type) const\n"); + return 0; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___setitem____SWIG_2(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double >::difference_type arg2 ; + std::vector< double >::value_type *arg3 = 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + ptrdiff_t val2 ; + int ecode2 = 0 ; + std::vector< double >::value_type temp3 ; + double val3 ; + int ecode3 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + PyObject * obj2 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OOO:VectorDouble___setitem__",&obj0,&obj1,&obj2)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble___setitem__" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + ecode2 = SWIG_AsVal_ptrdiff_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble___setitem__" "', argument " "2"" of type '" "std::vector< double >::difference_type""'"); + } + arg2 = static_cast< std::vector< double >::difference_type >(val2); + ecode3 = SWIG_AsVal_double(obj2, &val3); + if (!SWIG_IsOK(ecode3)) { + SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "VectorDouble___setitem__" "', argument " "3"" of type '" "std::vector< double >::value_type""'"); + } + temp3 = static_cast< std::vector< double >::value_type >(val3); + arg3 = &temp3; + try { + std_vector_Sl_double_Sg____setitem____SWIG_2(arg1,arg2,(double const &)*arg3); + } + catch(std::out_of_range &_e) { + SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); + } + + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble___setitem__(PyObject *self, PyObject *args) { + Py_ssize_t argc; + PyObject *argv[4] = { + 0 + }; + Py_ssize_t ii; + + if (!PyTuple_Check(args)) SWIG_fail; + argc = args ? PyObject_Length(args) : 0; + for (ii = 0; (ii < 3) && (ii < argc); ii++) { + argv[ii] = PyTuple_GET_ITEM(args,ii); + } + if (argc == 2) { + int _v; + int res = swig::asptr(argv[0], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + { + _v = PySlice_Check(argv[1]); + } + if (_v) { + return _wrap_VectorDouble___setitem____SWIG_1(self, args); + } + } + } + if (argc == 3) { + int _v; + int res = swig::asptr(argv[0], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + { + _v = PySlice_Check(argv[1]); + } + if (_v) { + int res = swig::asptr(argv[2], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + return _wrap_VectorDouble___setitem____SWIG_0(self, args); + } + } + } + } + if (argc == 3) { + int _v; + int res = swig::asptr(argv[0], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + { + int res = SWIG_AsVal_ptrdiff_t(argv[1], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + { + int res = SWIG_AsVal_double(argv[2], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + return _wrap_VectorDouble___setitem____SWIG_2(self, args); + } + } + } + } + +fail: + SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number or type of arguments for overloaded function 'VectorDouble___setitem__'.\n" + " Possible C/C++ prototypes are:\n" + " std::vector< double >::__setitem__(PySliceObject *,std::vector< double,std::allocator< double > > const &)\n" + " std::vector< double >::__setitem__(PySliceObject *)\n" + " std::vector< double >::__setitem__(std::vector< double >::difference_type,std::vector< double >::value_type const &)\n"); + return 0; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_pop(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + std::vector< double >::value_type result; + + if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_pop",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_pop" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + try { + result = (std::vector< double >::value_type)std_vector_Sl_double_Sg__pop(arg1); + } + catch(std::out_of_range &_e) { + SWIG_exception_fail(SWIG_IndexError, (&_e)->what()); + } + + resultobj = SWIG_From_double(static_cast< double >(result)); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_append(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double >::value_type *arg2 = 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + std::vector< double >::value_type temp2 ; + double val2 ; + int ecode2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble_append",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_append" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + ecode2 = SWIG_AsVal_double(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble_append" "', argument " "2"" of type '" "std::vector< double >::value_type""'"); + } + temp2 = static_cast< std::vector< double >::value_type >(val2); + arg2 = &temp2; + std_vector_Sl_double_Sg__append(arg1,(double const &)*arg2); + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_new_VectorDouble__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)":new_VectorDouble")) SWIG_fail; + result = (std::vector< double > *)new std::vector< double >(); + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, SWIG_POINTER_NEW | 0 ); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_new_VectorDouble__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = 0 ; + int res1 = SWIG_OLDOBJ ; + PyObject * obj0 = 0 ; + std::vector< double > *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"O:new_VectorDouble",&obj0)) SWIG_fail; + { + std::vector< double,std::allocator< double > > *ptr = (std::vector< double,std::allocator< double > > *)0; + res1 = swig::asptr(obj0, &ptr); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "new_VectorDouble" "', argument " "1"" of type '" "std::vector< double > const &""'"); + } + if (!ptr) { + SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "new_VectorDouble" "', argument " "1"" of type '" "std::vector< double > const &""'"); + } + arg1 = ptr; + } + result = (std::vector< double > *)new std::vector< double >((std::vector< double > const &)*arg1); + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, SWIG_POINTER_NEW | 0 ); + if (SWIG_IsNewObj(res1)) delete arg1; + return resultobj; +fail: + if (SWIG_IsNewObj(res1)) delete arg1; + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_empty(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + bool result; + + if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_empty",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_empty" "', argument " "1"" of type '" "std::vector< double > const *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + result = (bool)((std::vector< double > const *)arg1)->empty(); + resultobj = SWIG_From_bool(static_cast< bool >(result)); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_size(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + std::vector< double >::size_type result; + + if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_size",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_size" "', argument " "1"" of type '" "std::vector< double > const *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + result = ((std::vector< double > const *)arg1)->size(); + resultobj = SWIG_From_size_t(static_cast< size_t >(result)); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_swap(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double > *arg2 = 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + void *argp2 = 0 ; + int res2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble_swap",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_swap" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + res2 = SWIG_ConvertPtr(obj1, &argp2, SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 ); + if (!SWIG_IsOK(res2)) { + SWIG_exception_fail(SWIG_ArgError(res2), "in method '" "VectorDouble_swap" "', argument " "2"" of type '" "std::vector< double > &""'"); + } + if (!argp2) { + SWIG_exception_fail(SWIG_ValueError, "invalid null reference " "in method '" "VectorDouble_swap" "', argument " "2"" of type '" "std::vector< double > &""'"); + } + arg2 = reinterpret_cast< std::vector< double > * >(argp2); + (arg1)->swap(*arg2); + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_begin(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + std::vector< double >::iterator result; + + if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_begin",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_begin" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + result = (arg1)->begin(); + resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector< double >::iterator & >(result)), + swig::SwigPyIterator::descriptor(),SWIG_POINTER_OWN); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_end(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + std::vector< double >::iterator result; + + if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_end",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_end" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + result = (arg1)->end(); + resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector< double >::iterator & >(result)), + swig::SwigPyIterator::descriptor(),SWIG_POINTER_OWN); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_rbegin(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + std::vector< double >::reverse_iterator result; + + if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_rbegin",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_rbegin" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + result = (arg1)->rbegin(); + resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector< double >::reverse_iterator & >(result)), + swig::SwigPyIterator::descriptor(),SWIG_POINTER_OWN); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_rend(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + std::vector< double >::reverse_iterator result; + + if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_rend",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_rend" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + result = (arg1)->rend(); + resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector< double >::reverse_iterator & >(result)), + swig::SwigPyIterator::descriptor(),SWIG_POINTER_OWN); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_clear(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_clear",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_clear" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + (arg1)->clear(); + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_get_allocator(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + SwigValueWrapper< std::allocator< double > > result; + + if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_get_allocator",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_get_allocator" "', argument " "1"" of type '" "std::vector< double > const *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + result = ((std::vector< double > const *)arg1)->get_allocator(); + resultobj = SWIG_NewPointerObj((new std::vector< double >::allocator_type(static_cast< const std::vector< double >::allocator_type& >(result))), SWIGTYPE_p_std__allocatorT_double_t, SWIG_POINTER_OWN | 0 ); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_new_VectorDouble__SWIG_2(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double >::size_type arg1 ; + size_t val1 ; + int ecode1 = 0 ; + PyObject * obj0 = 0 ; + std::vector< double > *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"O:new_VectorDouble",&obj0)) SWIG_fail; + ecode1 = SWIG_AsVal_size_t(obj0, &val1); + if (!SWIG_IsOK(ecode1)) { + SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "new_VectorDouble" "', argument " "1"" of type '" "std::vector< double >::size_type""'"); + } + arg1 = static_cast< std::vector< double >::size_type >(val1); + result = (std::vector< double > *)new std::vector< double >(arg1); + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, SWIG_POINTER_NEW | 0 ); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_pop_back(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_pop_back",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_pop_back" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + (arg1)->pop_back(); + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_resize__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double >::size_type arg2 ; + void *argp1 = 0 ; + int res1 = 0 ; + size_t val2 ; + int ecode2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble_resize",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_resize" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + ecode2 = SWIG_AsVal_size_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble_resize" "', argument " "2"" of type '" "std::vector< double >::size_type""'"); + } + arg2 = static_cast< std::vector< double >::size_type >(val2); + (arg1)->resize(arg2); + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_erase__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double >::iterator arg2 ; + void *argp1 = 0 ; + int res1 = 0 ; + swig::SwigPyIterator *iter2 = 0 ; + int res2 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + std::vector< double >::iterator result; + + if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble_erase",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_erase" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + res2 = SWIG_ConvertPtr(obj1, SWIG_as_voidptrptr(&iter2), swig::SwigPyIterator::descriptor(), 0); + if (!SWIG_IsOK(res2) || !iter2) { + SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_erase" "', argument " "2"" of type '" "std::vector< double >::iterator""'"); + } else { + swig::SwigPyIterator_T::iterator > *iter_t = dynamic_cast::iterator > *>(iter2); + if (iter_t) { + arg2 = iter_t->get_current(); + } else { + SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_erase" "', argument " "2"" of type '" "std::vector< double >::iterator""'"); + } + } + result = std_vector_Sl_double_Sg__erase__SWIG_0(arg1,arg2); + resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector< double >::iterator & >(result)), + swig::SwigPyIterator::descriptor(),SWIG_POINTER_OWN); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_erase__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double >::iterator arg2 ; + std::vector< double >::iterator arg3 ; + void *argp1 = 0 ; + int res1 = 0 ; + swig::SwigPyIterator *iter2 = 0 ; + int res2 ; + swig::SwigPyIterator *iter3 = 0 ; + int res3 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + PyObject * obj2 = 0 ; + std::vector< double >::iterator result; + + if (!PyArg_ParseTuple(args,(char *)"OOO:VectorDouble_erase",&obj0,&obj1,&obj2)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_erase" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + res2 = SWIG_ConvertPtr(obj1, SWIG_as_voidptrptr(&iter2), swig::SwigPyIterator::descriptor(), 0); + if (!SWIG_IsOK(res2) || !iter2) { + SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_erase" "', argument " "2"" of type '" "std::vector< double >::iterator""'"); + } else { + swig::SwigPyIterator_T::iterator > *iter_t = dynamic_cast::iterator > *>(iter2); + if (iter_t) { + arg2 = iter_t->get_current(); + } else { + SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_erase" "', argument " "2"" of type '" "std::vector< double >::iterator""'"); + } + } + res3 = SWIG_ConvertPtr(obj2, SWIG_as_voidptrptr(&iter3), swig::SwigPyIterator::descriptor(), 0); + if (!SWIG_IsOK(res3) || !iter3) { + SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_erase" "', argument " "3"" of type '" "std::vector< double >::iterator""'"); + } else { + swig::SwigPyIterator_T::iterator > *iter_t = dynamic_cast::iterator > *>(iter3); + if (iter_t) { + arg3 = iter_t->get_current(); + } else { + SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_erase" "', argument " "3"" of type '" "std::vector< double >::iterator""'"); + } + } + result = std_vector_Sl_double_Sg__erase__SWIG_1(arg1,arg2,arg3); + resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector< double >::iterator & >(result)), + swig::SwigPyIterator::descriptor(),SWIG_POINTER_OWN); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_erase(PyObject *self, PyObject *args) { + Py_ssize_t argc; + PyObject *argv[4] = { + 0 + }; + Py_ssize_t ii; + + if (!PyTuple_Check(args)) SWIG_fail; + argc = args ? PyObject_Length(args) : 0; + for (ii = 0; (ii < 3) && (ii < argc); ii++) { + argv[ii] = PyTuple_GET_ITEM(args,ii); + } + if (argc == 2) { + int _v; + int res = swig::asptr(argv[0], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + swig::SwigPyIterator *iter = 0; + int res = SWIG_ConvertPtr(argv[1], SWIG_as_voidptrptr(&iter), swig::SwigPyIterator::descriptor(), 0); + _v = (SWIG_IsOK(res) && iter && (dynamic_cast::iterator > *>(iter) != 0)); + if (_v) { + return _wrap_VectorDouble_erase__SWIG_0(self, args); + } + } + } + if (argc == 3) { + int _v; + int res = swig::asptr(argv[0], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + swig::SwigPyIterator *iter = 0; + int res = SWIG_ConvertPtr(argv[1], SWIG_as_voidptrptr(&iter), swig::SwigPyIterator::descriptor(), 0); + _v = (SWIG_IsOK(res) && iter && (dynamic_cast::iterator > *>(iter) != 0)); + if (_v) { + swig::SwigPyIterator *iter = 0; + int res = SWIG_ConvertPtr(argv[2], SWIG_as_voidptrptr(&iter), swig::SwigPyIterator::descriptor(), 0); + _v = (SWIG_IsOK(res) && iter && (dynamic_cast::iterator > *>(iter) != 0)); + if (_v) { + return _wrap_VectorDouble_erase__SWIG_1(self, args); + } + } + } + } + +fail: + SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number or type of arguments for overloaded function 'VectorDouble_erase'.\n" + " Possible C/C++ prototypes are:\n" + " std::vector< double >::erase(std::vector< double >::iterator)\n" + " std::vector< double >::erase(std::vector< double >::iterator,std::vector< double >::iterator)\n"); + return 0; +} + + +SWIGINTERN PyObject *_wrap_new_VectorDouble__SWIG_3(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double >::size_type arg1 ; + std::vector< double >::value_type *arg2 = 0 ; + size_t val1 ; + int ecode1 = 0 ; + std::vector< double >::value_type temp2 ; + double val2 ; + int ecode2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + std::vector< double > *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:new_VectorDouble",&obj0,&obj1)) SWIG_fail; + ecode1 = SWIG_AsVal_size_t(obj0, &val1); + if (!SWIG_IsOK(ecode1)) { + SWIG_exception_fail(SWIG_ArgError(ecode1), "in method '" "new_VectorDouble" "', argument " "1"" of type '" "std::vector< double >::size_type""'"); + } + arg1 = static_cast< std::vector< double >::size_type >(val1); + ecode2 = SWIG_AsVal_double(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "new_VectorDouble" "', argument " "2"" of type '" "std::vector< double >::value_type""'"); + } + temp2 = static_cast< std::vector< double >::value_type >(val2); + arg2 = &temp2; + result = (std::vector< double > *)new std::vector< double >(arg1,(std::vector< double >::value_type const &)*arg2); + resultobj = SWIG_NewPointerObj(SWIG_as_voidptr(result), SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, SWIG_POINTER_NEW | 0 ); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_new_VectorDouble(PyObject *self, PyObject *args) { + Py_ssize_t argc; + PyObject *argv[3] = { + 0 + }; + Py_ssize_t ii; + + if (!PyTuple_Check(args)) SWIG_fail; + argc = args ? PyObject_Length(args) : 0; + for (ii = 0; (ii < 2) && (ii < argc); ii++) { + argv[ii] = PyTuple_GET_ITEM(args,ii); + } + if (argc == 0) { + return _wrap_new_VectorDouble__SWIG_0(self, args); + } + if (argc == 1) { + int _v; + { + int res = SWIG_AsVal_size_t(argv[0], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + return _wrap_new_VectorDouble__SWIG_2(self, args); + } + } + if (argc == 1) { + int _v; + int res = swig::asptr(argv[0], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + return _wrap_new_VectorDouble__SWIG_1(self, args); + } + } + if (argc == 2) { + int _v; + { + int res = SWIG_AsVal_size_t(argv[0], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + { + int res = SWIG_AsVal_double(argv[1], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + return _wrap_new_VectorDouble__SWIG_3(self, args); + } + } + } + +fail: + SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number or type of arguments for overloaded function 'new_VectorDouble'.\n" + " Possible C/C++ prototypes are:\n" + " std::vector< double >::vector()\n" + " std::vector< double >::vector(std::vector< double > const &)\n" + " std::vector< double >::vector(std::vector< double >::size_type)\n" + " std::vector< double >::vector(std::vector< double >::size_type,std::vector< double >::value_type const &)\n"); + return 0; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_push_back(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double >::value_type *arg2 = 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + std::vector< double >::value_type temp2 ; + double val2 ; + int ecode2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble_push_back",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_push_back" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + ecode2 = SWIG_AsVal_double(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble_push_back" "', argument " "2"" of type '" "std::vector< double >::value_type""'"); + } + temp2 = static_cast< std::vector< double >::value_type >(val2); + arg2 = &temp2; + (arg1)->push_back((std::vector< double >::value_type const &)*arg2); + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_front(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + std::vector< double >::value_type *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_front",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_front" "', argument " "1"" of type '" "std::vector< double > const *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + result = (std::vector< double >::value_type *) &((std::vector< double > const *)arg1)->front(); + resultobj = SWIG_From_double(static_cast< double >(*result)); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_back(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + std::vector< double >::value_type *result = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_back",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_back" "', argument " "1"" of type '" "std::vector< double > const *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + result = (std::vector< double >::value_type *) &((std::vector< double > const *)arg1)->back(); + resultobj = SWIG_From_double(static_cast< double >(*result)); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_assign(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double >::size_type arg2 ; + std::vector< double >::value_type *arg3 = 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + size_t val2 ; + int ecode2 = 0 ; + std::vector< double >::value_type temp3 ; + double val3 ; + int ecode3 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + PyObject * obj2 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OOO:VectorDouble_assign",&obj0,&obj1,&obj2)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_assign" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + ecode2 = SWIG_AsVal_size_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble_assign" "', argument " "2"" of type '" "std::vector< double >::size_type""'"); + } + arg2 = static_cast< std::vector< double >::size_type >(val2); + ecode3 = SWIG_AsVal_double(obj2, &val3); + if (!SWIG_IsOK(ecode3)) { + SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "VectorDouble_assign" "', argument " "3"" of type '" "std::vector< double >::value_type""'"); + } + temp3 = static_cast< std::vector< double >::value_type >(val3); + arg3 = &temp3; + (arg1)->assign(arg2,(std::vector< double >::value_type const &)*arg3); + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_resize__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double >::size_type arg2 ; + std::vector< double >::value_type *arg3 = 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + size_t val2 ; + int ecode2 = 0 ; + std::vector< double >::value_type temp3 ; + double val3 ; + int ecode3 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + PyObject * obj2 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OOO:VectorDouble_resize",&obj0,&obj1,&obj2)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_resize" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + ecode2 = SWIG_AsVal_size_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble_resize" "', argument " "2"" of type '" "std::vector< double >::size_type""'"); + } + arg2 = static_cast< std::vector< double >::size_type >(val2); + ecode3 = SWIG_AsVal_double(obj2, &val3); + if (!SWIG_IsOK(ecode3)) { + SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "VectorDouble_resize" "', argument " "3"" of type '" "std::vector< double >::value_type""'"); + } + temp3 = static_cast< std::vector< double >::value_type >(val3); + arg3 = &temp3; + (arg1)->resize(arg2,(std::vector< double >::value_type const &)*arg3); + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_resize(PyObject *self, PyObject *args) { + Py_ssize_t argc; + PyObject *argv[4] = { + 0 + }; + Py_ssize_t ii; + + if (!PyTuple_Check(args)) SWIG_fail; + argc = args ? PyObject_Length(args) : 0; + for (ii = 0; (ii < 3) && (ii < argc); ii++) { + argv[ii] = PyTuple_GET_ITEM(args,ii); + } + if (argc == 2) { + int _v; + int res = swig::asptr(argv[0], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + { + int res = SWIG_AsVal_size_t(argv[1], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + return _wrap_VectorDouble_resize__SWIG_0(self, args); + } + } + } + if (argc == 3) { + int _v; + int res = swig::asptr(argv[0], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + { + int res = SWIG_AsVal_size_t(argv[1], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + { + int res = SWIG_AsVal_double(argv[2], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + return _wrap_VectorDouble_resize__SWIG_1(self, args); + } + } + } + } + +fail: + SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number or type of arguments for overloaded function 'VectorDouble_resize'.\n" + " Possible C/C++ prototypes are:\n" + " std::vector< double >::resize(std::vector< double >::size_type)\n" + " std::vector< double >::resize(std::vector< double >::size_type,std::vector< double >::value_type const &)\n"); + return 0; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_insert__SWIG_0(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double >::iterator arg2 ; + std::vector< double >::value_type *arg3 = 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + swig::SwigPyIterator *iter2 = 0 ; + int res2 ; + std::vector< double >::value_type temp3 ; + double val3 ; + int ecode3 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + PyObject * obj2 = 0 ; + std::vector< double >::iterator result; + + if (!PyArg_ParseTuple(args,(char *)"OOO:VectorDouble_insert",&obj0,&obj1,&obj2)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_insert" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + res2 = SWIG_ConvertPtr(obj1, SWIG_as_voidptrptr(&iter2), swig::SwigPyIterator::descriptor(), 0); + if (!SWIG_IsOK(res2) || !iter2) { + SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_insert" "', argument " "2"" of type '" "std::vector< double >::iterator""'"); + } else { + swig::SwigPyIterator_T::iterator > *iter_t = dynamic_cast::iterator > *>(iter2); + if (iter_t) { + arg2 = iter_t->get_current(); + } else { + SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_insert" "', argument " "2"" of type '" "std::vector< double >::iterator""'"); + } + } + ecode3 = SWIG_AsVal_double(obj2, &val3); + if (!SWIG_IsOK(ecode3)) { + SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "VectorDouble_insert" "', argument " "3"" of type '" "std::vector< double >::value_type""'"); + } + temp3 = static_cast< std::vector< double >::value_type >(val3); + arg3 = &temp3; + result = std_vector_Sl_double_Sg__insert__SWIG_0(arg1,arg2,(double const &)*arg3); + resultobj = SWIG_NewPointerObj(swig::make_output_iterator(static_cast< const std::vector< double >::iterator & >(result)), + swig::SwigPyIterator::descriptor(),SWIG_POINTER_OWN); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_insert__SWIG_1(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double >::iterator arg2 ; + std::vector< double >::size_type arg3 ; + std::vector< double >::value_type *arg4 = 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + swig::SwigPyIterator *iter2 = 0 ; + int res2 ; + size_t val3 ; + int ecode3 = 0 ; + std::vector< double >::value_type temp4 ; + double val4 ; + int ecode4 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + PyObject * obj2 = 0 ; + PyObject * obj3 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OOOO:VectorDouble_insert",&obj0,&obj1,&obj2,&obj3)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_insert" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + res2 = SWIG_ConvertPtr(obj1, SWIG_as_voidptrptr(&iter2), swig::SwigPyIterator::descriptor(), 0); + if (!SWIG_IsOK(res2) || !iter2) { + SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_insert" "', argument " "2"" of type '" "std::vector< double >::iterator""'"); + } else { + swig::SwigPyIterator_T::iterator > *iter_t = dynamic_cast::iterator > *>(iter2); + if (iter_t) { + arg2 = iter_t->get_current(); + } else { + SWIG_exception_fail(SWIG_ArgError(SWIG_TypeError), "in method '" "VectorDouble_insert" "', argument " "2"" of type '" "std::vector< double >::iterator""'"); + } + } + ecode3 = SWIG_AsVal_size_t(obj2, &val3); + if (!SWIG_IsOK(ecode3)) { + SWIG_exception_fail(SWIG_ArgError(ecode3), "in method '" "VectorDouble_insert" "', argument " "3"" of type '" "std::vector< double >::size_type""'"); + } + arg3 = static_cast< std::vector< double >::size_type >(val3); + ecode4 = SWIG_AsVal_double(obj3, &val4); + if (!SWIG_IsOK(ecode4)) { + SWIG_exception_fail(SWIG_ArgError(ecode4), "in method '" "VectorDouble_insert" "', argument " "4"" of type '" "std::vector< double >::value_type""'"); + } + temp4 = static_cast< std::vector< double >::value_type >(val4); + arg4 = &temp4; + std_vector_Sl_double_Sg__insert__SWIG_1(arg1,arg2,arg3,(double const &)*arg4); + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_insert(PyObject *self, PyObject *args) { + Py_ssize_t argc; + PyObject *argv[5] = { + 0 + }; + Py_ssize_t ii; + + if (!PyTuple_Check(args)) SWIG_fail; + argc = args ? PyObject_Length(args) : 0; + for (ii = 0; (ii < 4) && (ii < argc); ii++) { + argv[ii] = PyTuple_GET_ITEM(args,ii); + } + if (argc == 3) { + int _v; + int res = swig::asptr(argv[0], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + swig::SwigPyIterator *iter = 0; + int res = SWIG_ConvertPtr(argv[1], SWIG_as_voidptrptr(&iter), swig::SwigPyIterator::descriptor(), 0); + _v = (SWIG_IsOK(res) && iter && (dynamic_cast::iterator > *>(iter) != 0)); + if (_v) { + { + int res = SWIG_AsVal_double(argv[2], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + return _wrap_VectorDouble_insert__SWIG_0(self, args); + } + } + } + } + if (argc == 4) { + int _v; + int res = swig::asptr(argv[0], (std::vector< double,std::allocator< double > >**)(0)); + _v = SWIG_CheckState(res); + if (_v) { + swig::SwigPyIterator *iter = 0; + int res = SWIG_ConvertPtr(argv[1], SWIG_as_voidptrptr(&iter), swig::SwigPyIterator::descriptor(), 0); + _v = (SWIG_IsOK(res) && iter && (dynamic_cast::iterator > *>(iter) != 0)); + if (_v) { + { + int res = SWIG_AsVal_size_t(argv[2], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + { + int res = SWIG_AsVal_double(argv[3], NULL); + _v = SWIG_CheckState(res); + } + if (_v) { + return _wrap_VectorDouble_insert__SWIG_1(self, args); + } + } + } + } + } + +fail: + SWIG_SetErrorMsg(PyExc_NotImplementedError,"Wrong number or type of arguments for overloaded function 'VectorDouble_insert'.\n" + " Possible C/C++ prototypes are:\n" + " std::vector< double >::insert(std::vector< double >::iterator,std::vector< double >::value_type const &)\n" + " std::vector< double >::insert(std::vector< double >::iterator,std::vector< double >::size_type,std::vector< double >::value_type const &)\n"); + return 0; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_reserve(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + std::vector< double >::size_type arg2 ; + void *argp1 = 0 ; + int res1 = 0 ; + size_t val2 ; + int ecode2 = 0 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"OO:VectorDouble_reserve",&obj0,&obj1)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_reserve" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + ecode2 = SWIG_AsVal_size_t(obj1, &val2); + if (!SWIG_IsOK(ecode2)) { + SWIG_exception_fail(SWIG_ArgError(ecode2), "in method '" "VectorDouble_reserve" "', argument " "2"" of type '" "std::vector< double >::size_type""'"); + } + arg2 = static_cast< std::vector< double >::size_type >(val2); + (arg1)->reserve(arg2); + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_VectorDouble_capacity(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + std::vector< double >::size_type result; + + if (!PyArg_ParseTuple(args,(char *)"O:VectorDouble_capacity",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, 0 | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "VectorDouble_capacity" "', argument " "1"" of type '" "std::vector< double > const *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + result = ((std::vector< double > const *)arg1)->capacity(); + resultobj = SWIG_From_size_t(static_cast< size_t >(result)); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *_wrap_delete_VectorDouble(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double > *arg1 = (std::vector< double > *) 0 ; + void *argp1 = 0 ; + int res1 = 0 ; + PyObject * obj0 = 0 ; + + if (!PyArg_ParseTuple(args,(char *)"O:delete_VectorDouble",&obj0)) SWIG_fail; + res1 = SWIG_ConvertPtr(obj0, &argp1,SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, SWIG_POINTER_DISOWN | 0 ); + if (!SWIG_IsOK(res1)) { + SWIG_exception_fail(SWIG_ArgError(res1), "in method '" "delete_VectorDouble" "', argument " "1"" of type '" "std::vector< double > *""'"); + } + arg1 = reinterpret_cast< std::vector< double > * >(argp1); + delete arg1; + resultobj = SWIG_Py_Void(); + return resultobj; +fail: + return NULL; +} + + +SWIGINTERN PyObject *VectorDouble_swigregister(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *obj; + if (!PyArg_ParseTuple(args,(char*)"O:swigregister", &obj)) return NULL; + SWIG_TypeNewClientData(SWIGTYPE_p_std__vectorT_double_std__allocatorT_double_t_t, SWIG_NewClientData(obj)); + return SWIG_Py_Void(); +} + +SWIGINTERN PyObject *_wrap_iou_poly(PyObject *SWIGUNUSEDPARM(self), PyObject *args) { + PyObject *resultobj = 0; + std::vector< double,std::allocator< double > > arg1 ; + std::vector< double,std::allocator< double > > arg2 ; + PyObject * obj0 = 0 ; + PyObject * obj1 = 0 ; + double result; + + if (!PyArg_ParseTuple(args,(char *)"OO:iou_poly",&obj0,&obj1)) SWIG_fail; + { + std::vector< double,std::allocator< double > > *ptr = (std::vector< double,std::allocator< double > > *)0; + int res = swig::asptr(obj0, &ptr); + if (!SWIG_IsOK(res) || !ptr) { + SWIG_exception_fail(SWIG_ArgError((ptr ? res : SWIG_TypeError)), "in method '" "iou_poly" "', argument " "1"" of type '" "std::vector< double,std::allocator< double > >""'"); + } + arg1 = *ptr; + if (SWIG_IsNewObj(res)) delete ptr; + } + { + std::vector< double,std::allocator< double > > *ptr = (std::vector< double,std::allocator< double > > *)0; + int res = swig::asptr(obj1, &ptr); + if (!SWIG_IsOK(res) || !ptr) { + SWIG_exception_fail(SWIG_ArgError((ptr ? res : SWIG_TypeError)), "in method '" "iou_poly" "', argument " "2"" of type '" "std::vector< double,std::allocator< double > >""'"); + } + arg2 = *ptr; + if (SWIG_IsNewObj(res)) delete ptr; + } + result = (double)iou_poly(arg1,arg2); + resultobj = SWIG_From_double(static_cast< double >(result)); + return resultobj; +fail: + return NULL; +} + + +static PyMethodDef SwigMethods[] = { + { (char *)"SWIG_PyInstanceMethod_New", (PyCFunction)SWIG_PyInstanceMethod_New, METH_O, NULL}, + { (char *)"delete_SwigPyIterator", _wrap_delete_SwigPyIterator, METH_VARARGS, NULL}, + { (char *)"SwigPyIterator_value", _wrap_SwigPyIterator_value, METH_VARARGS, NULL}, + { (char *)"SwigPyIterator_incr", _wrap_SwigPyIterator_incr, METH_VARARGS, NULL}, + { (char *)"SwigPyIterator_decr", _wrap_SwigPyIterator_decr, METH_VARARGS, NULL}, + { (char *)"SwigPyIterator_distance", _wrap_SwigPyIterator_distance, METH_VARARGS, NULL}, + { (char *)"SwigPyIterator_equal", _wrap_SwigPyIterator_equal, METH_VARARGS, NULL}, + { (char *)"SwigPyIterator_copy", _wrap_SwigPyIterator_copy, METH_VARARGS, NULL}, + { (char *)"SwigPyIterator_next", _wrap_SwigPyIterator_next, METH_VARARGS, NULL}, + { (char *)"SwigPyIterator___next__", _wrap_SwigPyIterator___next__, METH_VARARGS, NULL}, + { (char *)"SwigPyIterator_previous", _wrap_SwigPyIterator_previous, METH_VARARGS, NULL}, + { (char *)"SwigPyIterator_advance", _wrap_SwigPyIterator_advance, METH_VARARGS, NULL}, + { (char *)"SwigPyIterator___eq__", _wrap_SwigPyIterator___eq__, METH_VARARGS, NULL}, + { (char *)"SwigPyIterator___ne__", _wrap_SwigPyIterator___ne__, METH_VARARGS, NULL}, + { (char *)"SwigPyIterator___iadd__", _wrap_SwigPyIterator___iadd__, METH_VARARGS, NULL}, + { (char *)"SwigPyIterator___isub__", _wrap_SwigPyIterator___isub__, METH_VARARGS, NULL}, + { (char *)"SwigPyIterator___add__", _wrap_SwigPyIterator___add__, METH_VARARGS, NULL}, + { (char *)"SwigPyIterator___sub__", _wrap_SwigPyIterator___sub__, METH_VARARGS, NULL}, + { (char *)"SwigPyIterator_swigregister", SwigPyIterator_swigregister, METH_VARARGS, NULL}, + { (char *)"VectorDouble_iterator", _wrap_VectorDouble_iterator, METH_VARARGS, NULL}, + { (char *)"VectorDouble___nonzero__", _wrap_VectorDouble___nonzero__, METH_VARARGS, NULL}, + { (char *)"VectorDouble___bool__", _wrap_VectorDouble___bool__, METH_VARARGS, NULL}, + { (char *)"VectorDouble___len__", _wrap_VectorDouble___len__, METH_VARARGS, NULL}, + { (char *)"VectorDouble___getslice__", _wrap_VectorDouble___getslice__, METH_VARARGS, NULL}, + { (char *)"VectorDouble___setslice__", _wrap_VectorDouble___setslice__, METH_VARARGS, NULL}, + { (char *)"VectorDouble___delslice__", _wrap_VectorDouble___delslice__, METH_VARARGS, NULL}, + { (char *)"VectorDouble___delitem__", _wrap_VectorDouble___delitem__, METH_VARARGS, NULL}, + { (char *)"VectorDouble___getitem__", _wrap_VectorDouble___getitem__, METH_VARARGS, NULL}, + { (char *)"VectorDouble___setitem__", _wrap_VectorDouble___setitem__, METH_VARARGS, NULL}, + { (char *)"VectorDouble_pop", _wrap_VectorDouble_pop, METH_VARARGS, NULL}, + { (char *)"VectorDouble_append", _wrap_VectorDouble_append, METH_VARARGS, NULL}, + { (char *)"VectorDouble_empty", _wrap_VectorDouble_empty, METH_VARARGS, NULL}, + { (char *)"VectorDouble_size", _wrap_VectorDouble_size, METH_VARARGS, NULL}, + { (char *)"VectorDouble_swap", _wrap_VectorDouble_swap, METH_VARARGS, NULL}, + { (char *)"VectorDouble_begin", _wrap_VectorDouble_begin, METH_VARARGS, NULL}, + { (char *)"VectorDouble_end", _wrap_VectorDouble_end, METH_VARARGS, NULL}, + { (char *)"VectorDouble_rbegin", _wrap_VectorDouble_rbegin, METH_VARARGS, NULL}, + { (char *)"VectorDouble_rend", _wrap_VectorDouble_rend, METH_VARARGS, NULL}, + { (char *)"VectorDouble_clear", _wrap_VectorDouble_clear, METH_VARARGS, NULL}, + { (char *)"VectorDouble_get_allocator", _wrap_VectorDouble_get_allocator, METH_VARARGS, NULL}, + { (char *)"VectorDouble_pop_back", _wrap_VectorDouble_pop_back, METH_VARARGS, NULL}, + { (char *)"VectorDouble_erase", _wrap_VectorDouble_erase, METH_VARARGS, NULL}, + { (char *)"new_VectorDouble", _wrap_new_VectorDouble, METH_VARARGS, NULL}, + { (char *)"VectorDouble_push_back", _wrap_VectorDouble_push_back, METH_VARARGS, NULL}, + { (char *)"VectorDouble_front", _wrap_VectorDouble_front, METH_VARARGS, NULL}, + { (char *)"VectorDouble_back", _wrap_VectorDouble_back, METH_VARARGS, NULL}, + { (char *)"VectorDouble_assign", _wrap_VectorDouble_assign, METH_VARARGS, NULL}, + { (char *)"VectorDouble_resize", _wrap_VectorDouble_resize, METH_VARARGS, NULL}, + { (char *)"VectorDouble_insert", _wrap_VectorDouble_insert, METH_VARARGS, NULL}, + { (char *)"VectorDouble_reserve", _wrap_VectorDouble_reserve, METH_VARARGS, NULL}, + { (char *)"VectorDouble_capacity", _wrap_VectorDouble_capacity, METH_VARARGS, NULL}, + { (char *)"delete_VectorDouble", _wrap_delete_VectorDouble, METH_VARARGS, NULL}, + { (char *)"VectorDouble_swigregister", VectorDouble_swigregister, METH_VARARGS, NULL}, + { (char *)"iou_poly", _wrap_iou_poly, METH_VARARGS, NULL}, + { NULL, NULL, 0, NULL } +}; + + +/* -------- TYPE CONVERSION AND EQUIVALENCE RULES (BEGIN) -------- */ + +static swig_type_info _swigt__p_allocator_type = {"_p_allocator_type", "allocator_type *", 0, 0, (void*)0, 0}; +static swig_type_info _swigt__p_char = {"_p_char", "char *", 0, 0, (void*)0, 0}; +static swig_type_info _swigt__p_difference_type = {"_p_difference_type", "difference_type *", 0, 0, (void*)0, 0}; +static swig_type_info _swigt__p_p_PyObject = {"_p_p_PyObject", "PyObject **", 0, 0, (void*)0, 0}; +static swig_type_info _swigt__p_size_type = {"_p_size_type", "size_type *", 0, 0, (void*)0, 0}; +static swig_type_info _swigt__p_std__allocatorT_double_t = {"_p_std__allocatorT_double_t", "std::vector< double >::allocator_type *|std::allocator< double > *", 0, 0, (void*)0, 0}; +static swig_type_info _swigt__p_std__invalid_argument = {"_p_std__invalid_argument", "std::invalid_argument *", 0, 0, (void*)0, 0}; +static swig_type_info _swigt__p_std__vectorT_double_std__allocatorT_double_t_t = {"_p_std__vectorT_double_std__allocatorT_double_t_t", "std::vector< double,std::allocator< double > > *|std::vector< double > *", 0, 0, (void*)0, 0}; +static swig_type_info _swigt__p_swig__SwigPyIterator = {"_p_swig__SwigPyIterator", "swig::SwigPyIterator *", 0, 0, (void*)0, 0}; +static swig_type_info _swigt__p_value_type = {"_p_value_type", "value_type *", 0, 0, (void*)0, 0}; + +static swig_type_info *swig_type_initial[] = { + &_swigt__p_allocator_type, + &_swigt__p_char, + &_swigt__p_difference_type, + &_swigt__p_p_PyObject, + &_swigt__p_size_type, + &_swigt__p_std__allocatorT_double_t, + &_swigt__p_std__invalid_argument, + &_swigt__p_std__vectorT_double_std__allocatorT_double_t_t, + &_swigt__p_swig__SwigPyIterator, + &_swigt__p_value_type, +}; + +static swig_cast_info _swigc__p_allocator_type[] = { {&_swigt__p_allocator_type, 0, 0, 0},{0, 0, 0, 0}}; +static swig_cast_info _swigc__p_char[] = { {&_swigt__p_char, 0, 0, 0},{0, 0, 0, 0}}; +static swig_cast_info _swigc__p_difference_type[] = { {&_swigt__p_difference_type, 0, 0, 0},{0, 0, 0, 0}}; +static swig_cast_info _swigc__p_p_PyObject[] = { {&_swigt__p_p_PyObject, 0, 0, 0},{0, 0, 0, 0}}; +static swig_cast_info _swigc__p_size_type[] = { {&_swigt__p_size_type, 0, 0, 0},{0, 0, 0, 0}}; +static swig_cast_info _swigc__p_std__allocatorT_double_t[] = { {&_swigt__p_std__allocatorT_double_t, 0, 0, 0},{0, 0, 0, 0}}; +static swig_cast_info _swigc__p_std__invalid_argument[] = { {&_swigt__p_std__invalid_argument, 0, 0, 0},{0, 0, 0, 0}}; +static swig_cast_info _swigc__p_std__vectorT_double_std__allocatorT_double_t_t[] = { {&_swigt__p_std__vectorT_double_std__allocatorT_double_t_t, 0, 0, 0},{0, 0, 0, 0}}; +static swig_cast_info _swigc__p_swig__SwigPyIterator[] = { {&_swigt__p_swig__SwigPyIterator, 0, 0, 0},{0, 0, 0, 0}}; +static swig_cast_info _swigc__p_value_type[] = { {&_swigt__p_value_type, 0, 0, 0},{0, 0, 0, 0}}; + +static swig_cast_info *swig_cast_initial[] = { + _swigc__p_allocator_type, + _swigc__p_char, + _swigc__p_difference_type, + _swigc__p_p_PyObject, + _swigc__p_size_type, + _swigc__p_std__allocatorT_double_t, + _swigc__p_std__invalid_argument, + _swigc__p_std__vectorT_double_std__allocatorT_double_t_t, + _swigc__p_swig__SwigPyIterator, + _swigc__p_value_type, +}; + + +/* -------- TYPE CONVERSION AND EQUIVALENCE RULES (END) -------- */ + +static swig_const_info swig_const_table[] = { +{0, 0, 0, 0.0, 0, 0}}; + +#ifdef __cplusplus +} +#endif +/* ----------------------------------------------------------------------------- + * Type initialization: + * This problem is tough by the requirement that no dynamic + * memory is used. Also, since swig_type_info structures store pointers to + * swig_cast_info structures and swig_cast_info structures store pointers back + * to swig_type_info structures, we need some lookup code at initialization. + * The idea is that swig generates all the structures that are needed. + * The runtime then collects these partially filled structures. + * The SWIG_InitializeModule function takes these initial arrays out of + * swig_module, and does all the lookup, filling in the swig_module.types + * array with the correct data and linking the correct swig_cast_info + * structures together. + * + * The generated swig_type_info structures are assigned statically to an initial + * array. We just loop through that array, and handle each type individually. + * First we lookup if this type has been already loaded, and if so, use the + * loaded structure instead of the generated one. Then we have to fill in the + * cast linked list. The cast data is initially stored in something like a + * two-dimensional array. Each row corresponds to a type (there are the same + * number of rows as there are in the swig_type_initial array). Each entry in + * a column is one of the swig_cast_info structures for that type. + * The cast_initial array is actually an array of arrays, because each row has + * a variable number of columns. So to actually build the cast linked list, + * we find the array of casts associated with the type, and loop through it + * adding the casts to the list. The one last trick we need to do is making + * sure the type pointer in the swig_cast_info struct is correct. + * + * First off, we lookup the cast->type name to see if it is already loaded. + * There are three cases to handle: + * 1) If the cast->type has already been loaded AND the type we are adding + * casting info to has not been loaded (it is in this module), THEN we + * replace the cast->type pointer with the type pointer that has already + * been loaded. + * 2) If BOTH types (the one we are adding casting info to, and the + * cast->type) are loaded, THEN the cast info has already been loaded by + * the previous module so we just ignore it. + * 3) Finally, if cast->type has not already been loaded, then we add that + * swig_cast_info to the linked list (because the cast->type) pointer will + * be correct. + * ----------------------------------------------------------------------------- */ + +#ifdef __cplusplus +extern "C" { +#if 0 +} /* c-mode */ +#endif +#endif + +#if 0 +#define SWIGRUNTIME_DEBUG +#endif + + +SWIGRUNTIME void +SWIG_InitializeModule(void *clientdata) { + size_t i; + swig_module_info *module_head, *iter; + int init; + + /* check to see if the circular list has been setup, if not, set it up */ + if (swig_module.next==0) { + /* Initialize the swig_module */ + swig_module.type_initial = swig_type_initial; + swig_module.cast_initial = swig_cast_initial; + swig_module.next = &swig_module; + init = 1; + } else { + init = 0; + } + + /* Try and load any already created modules */ + module_head = SWIG_GetModule(clientdata); + if (!module_head) { + /* This is the first module loaded for this interpreter */ + /* so set the swig module into the interpreter */ + SWIG_SetModule(clientdata, &swig_module); + } else { + /* the interpreter has loaded a SWIG module, but has it loaded this one? */ + iter=module_head; + do { + if (iter==&swig_module) { + /* Our module is already in the list, so there's nothing more to do. */ + return; + } + iter=iter->next; + } while (iter!= module_head); + + /* otherwise we must add our module into the list */ + swig_module.next = module_head->next; + module_head->next = &swig_module; + } + + /* When multiple interpreters are used, a module could have already been initialized in + a different interpreter, but not yet have a pointer in this interpreter. + In this case, we do not want to continue adding types... everything should be + set up already */ + if (init == 0) return; + + /* Now work on filling in swig_module.types */ +#ifdef SWIGRUNTIME_DEBUG + printf("SWIG_InitializeModule: size %d\n", swig_module.size); +#endif + for (i = 0; i < swig_module.size; ++i) { + swig_type_info *type = 0; + swig_type_info *ret; + swig_cast_info *cast; + +#ifdef SWIGRUNTIME_DEBUG + printf("SWIG_InitializeModule: type %d %s\n", i, swig_module.type_initial[i]->name); +#endif + + /* if there is another module already loaded */ + if (swig_module.next != &swig_module) { + type = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, swig_module.type_initial[i]->name); + } + if (type) { + /* Overwrite clientdata field */ +#ifdef SWIGRUNTIME_DEBUG + printf("SWIG_InitializeModule: found type %s\n", type->name); +#endif + if (swig_module.type_initial[i]->clientdata) { + type->clientdata = swig_module.type_initial[i]->clientdata; +#ifdef SWIGRUNTIME_DEBUG + printf("SWIG_InitializeModule: found and overwrite type %s \n", type->name); +#endif + } + } else { + type = swig_module.type_initial[i]; + } + + /* Insert casting types */ + cast = swig_module.cast_initial[i]; + while (cast->type) { + /* Don't need to add information already in the list */ + ret = 0; +#ifdef SWIGRUNTIME_DEBUG + printf("SWIG_InitializeModule: look cast %s\n", cast->type->name); +#endif + if (swig_module.next != &swig_module) { + ret = SWIG_MangledTypeQueryModule(swig_module.next, &swig_module, cast->type->name); +#ifdef SWIGRUNTIME_DEBUG + if (ret) printf("SWIG_InitializeModule: found cast %s\n", ret->name); +#endif + } + if (ret) { + if (type == swig_module.type_initial[i]) { +#ifdef SWIGRUNTIME_DEBUG + printf("SWIG_InitializeModule: skip old type %s\n", ret->name); +#endif + cast->type = ret; + ret = 0; + } else { + /* Check for casting already in the list */ + swig_cast_info *ocast = SWIG_TypeCheck(ret->name, type); +#ifdef SWIGRUNTIME_DEBUG + if (ocast) printf("SWIG_InitializeModule: skip old cast %s\n", ret->name); +#endif + if (!ocast) ret = 0; + } + } + + if (!ret) { +#ifdef SWIGRUNTIME_DEBUG + printf("SWIG_InitializeModule: adding cast %s\n", cast->type->name); +#endif + if (type->cast) { + type->cast->prev = cast; + cast->next = type->cast; + } + type->cast = cast; + } + cast++; + } + /* Set entry in modules->types array equal to the type */ + swig_module.types[i] = type; + } + swig_module.types[i] = 0; + +#ifdef SWIGRUNTIME_DEBUG + printf("**** SWIG_InitializeModule: Cast List ******\n"); + for (i = 0; i < swig_module.size; ++i) { + int j = 0; + swig_cast_info *cast = swig_module.cast_initial[i]; + printf("SWIG_InitializeModule: type %d %s\n", i, swig_module.type_initial[i]->name); + while (cast->type) { + printf("SWIG_InitializeModule: cast type %s\n", cast->type->name); + cast++; + ++j; + } + printf("---- Total casts: %d\n",j); + } + printf("**** SWIG_InitializeModule: Cast List ******\n"); +#endif +} + +/* This function will propagate the clientdata field of type to +* any new swig_type_info structures that have been added into the list +* of equivalent types. It is like calling +* SWIG_TypeClientData(type, clientdata) a second time. +*/ +SWIGRUNTIME void +SWIG_PropagateClientData(void) { + size_t i; + swig_cast_info *equiv; + static int init_run = 0; + + if (init_run) return; + init_run = 1; + + for (i = 0; i < swig_module.size; i++) { + if (swig_module.types[i]->clientdata) { + equiv = swig_module.types[i]->cast; + while (equiv) { + if (!equiv->converter) { + if (equiv->type && !equiv->type->clientdata) + SWIG_TypeClientData(equiv->type, swig_module.types[i]->clientdata); + } + equiv = equiv->next; + } + } + } +} + +#ifdef __cplusplus +#if 0 +{ + /* c-mode */ +#endif +} +#endif + + + +#ifdef __cplusplus +extern "C" { +#endif + + /* Python-specific SWIG API */ +#define SWIG_newvarlink() SWIG_Python_newvarlink() +#define SWIG_addvarlink(p, name, get_attr, set_attr) SWIG_Python_addvarlink(p, name, get_attr, set_attr) +#define SWIG_InstallConstants(d, constants) SWIG_Python_InstallConstants(d, constants) + + /* ----------------------------------------------------------------------------- + * global variable support code. + * ----------------------------------------------------------------------------- */ + + typedef struct swig_globalvar { + char *name; /* Name of global variable */ + PyObject *(*get_attr)(void); /* Return the current value */ + int (*set_attr)(PyObject *); /* Set the value */ + struct swig_globalvar *next; + } swig_globalvar; + + typedef struct swig_varlinkobject { + PyObject_HEAD + swig_globalvar *vars; + } swig_varlinkobject; + + SWIGINTERN PyObject * + swig_varlink_repr(swig_varlinkobject *SWIGUNUSEDPARM(v)) { +#if PY_VERSION_HEX >= 0x03000000 + return PyUnicode_InternFromString(""); +#else + return PyString_FromString(""); +#endif + } + + SWIGINTERN PyObject * + swig_varlink_str(swig_varlinkobject *v) { +#if PY_VERSION_HEX >= 0x03000000 + PyObject *str = PyUnicode_InternFromString("("); + PyObject *tail; + PyObject *joined; + swig_globalvar *var; + for (var = v->vars; var; var=var->next) { + tail = PyUnicode_FromString(var->name); + joined = PyUnicode_Concat(str, tail); + Py_DecRef(str); + Py_DecRef(tail); + str = joined; + if (var->next) { + tail = PyUnicode_InternFromString(", "); + joined = PyUnicode_Concat(str, tail); + Py_DecRef(str); + Py_DecRef(tail); + str = joined; + } + } + tail = PyUnicode_InternFromString(")"); + joined = PyUnicode_Concat(str, tail); + Py_DecRef(str); + Py_DecRef(tail); + str = joined; +#else + PyObject *str = PyString_FromString("("); + swig_globalvar *var; + for (var = v->vars; var; var=var->next) { + PyString_ConcatAndDel(&str,PyString_FromString(var->name)); + if (var->next) PyString_ConcatAndDel(&str,PyString_FromString(", ")); + } + PyString_ConcatAndDel(&str,PyString_FromString(")")); +#endif + return str; + } + + SWIGINTERN int + swig_varlink_print(swig_varlinkobject *v, FILE *fp, int SWIGUNUSEDPARM(flags)) { + char *tmp; + PyObject *str = swig_varlink_str(v); + fprintf(fp,"Swig global variables "); + fprintf(fp,"%s\n", tmp = SWIG_Python_str_AsChar(str)); + SWIG_Python_str_DelForPy3(tmp); + Py_DECREF(str); + return 0; + } + + SWIGINTERN void + swig_varlink_dealloc(swig_varlinkobject *v) { + swig_globalvar *var = v->vars; + while (var) { + swig_globalvar *n = var->next; + free(var->name); + free(var); + var = n; + } + } + + SWIGINTERN PyObject * + swig_varlink_getattr(swig_varlinkobject *v, char *n) { + PyObject *res = NULL; + swig_globalvar *var = v->vars; + while (var) { + if (strcmp(var->name,n) == 0) { + res = (*var->get_attr)(); + break; + } + var = var->next; + } + if (res == NULL && !PyErr_Occurred()) { + PyErr_Format(PyExc_AttributeError, "Unknown C global variable '%s'", n); + } + return res; + } + + SWIGINTERN int + swig_varlink_setattr(swig_varlinkobject *v, char *n, PyObject *p) { + int res = 1; + swig_globalvar *var = v->vars; + while (var) { + if (strcmp(var->name,n) == 0) { + res = (*var->set_attr)(p); + break; + } + var = var->next; + } + if (res == 1 && !PyErr_Occurred()) { + PyErr_Format(PyExc_AttributeError, "Unknown C global variable '%s'", n); + } + return res; + } + + SWIGINTERN PyTypeObject* + swig_varlink_type(void) { + static char varlink__doc__[] = "Swig var link object"; + static PyTypeObject varlink_type; + static int type_init = 0; + if (!type_init) { + const PyTypeObject tmp = { + /* PyObject header changed in Python 3 */ +#if PY_VERSION_HEX >= 0x03000000 + PyVarObject_HEAD_INIT(NULL, 0) +#else + PyObject_HEAD_INIT(NULL) + 0, /* ob_size */ +#endif + (char *)"swigvarlink", /* tp_name */ + sizeof(swig_varlinkobject), /* tp_basicsize */ + 0, /* tp_itemsize */ + (destructor) swig_varlink_dealloc, /* tp_dealloc */ + (printfunc) swig_varlink_print, /* tp_print */ + (getattrfunc) swig_varlink_getattr, /* tp_getattr */ + (setattrfunc) swig_varlink_setattr, /* tp_setattr */ + 0, /* tp_compare */ + (reprfunc) swig_varlink_repr, /* tp_repr */ + 0, /* tp_as_number */ + 0, /* tp_as_sequence */ + 0, /* tp_as_mapping */ + 0, /* tp_hash */ + 0, /* tp_call */ + (reprfunc) swig_varlink_str, /* tp_str */ + 0, /* tp_getattro */ + 0, /* tp_setattro */ + 0, /* tp_as_buffer */ + 0, /* tp_flags */ + varlink__doc__, /* tp_doc */ + 0, /* tp_traverse */ + 0, /* tp_clear */ + 0, /* tp_richcompare */ + 0, /* tp_weaklistoffset */ +#if PY_VERSION_HEX >= 0x02020000 + 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, /* tp_iter -> tp_weaklist */ +#endif +#if PY_VERSION_HEX >= 0x02030000 + 0, /* tp_del */ +#endif +#if PY_VERSION_HEX >= 0x02060000 + 0, /* tp_version_tag */ +#endif +#if PY_VERSION_HEX >= 0x03040000 + 0, /* tp_finalize */ +#endif +#ifdef COUNT_ALLOCS + 0, /* tp_allocs */ + 0, /* tp_frees */ + 0, /* tp_maxalloc */ +#if PY_VERSION_HEX >= 0x02050000 + 0, /* tp_prev */ +#endif + 0 /* tp_next */ +#endif + }; + varlink_type = tmp; + type_init = 1; +#if PY_VERSION_HEX < 0x02020000 + varlink_type.ob_type = &PyType_Type; +#else + if (PyType_Ready(&varlink_type) < 0) + return NULL; +#endif + } + return &varlink_type; + } + + /* Create a variable linking object for use later */ + SWIGINTERN PyObject * + SWIG_Python_newvarlink(void) { + swig_varlinkobject *result = PyObject_NEW(swig_varlinkobject, swig_varlink_type()); + if (result) { + result->vars = 0; + } + return ((PyObject*) result); + } + + SWIGINTERN void + SWIG_Python_addvarlink(PyObject *p, char *name, PyObject *(*get_attr)(void), int (*set_attr)(PyObject *p)) { + swig_varlinkobject *v = (swig_varlinkobject *) p; + swig_globalvar *gv = (swig_globalvar *) malloc(sizeof(swig_globalvar)); + if (gv) { + size_t size = strlen(name)+1; + gv->name = (char *)malloc(size); + if (gv->name) { + strncpy(gv->name,name,size); + gv->get_attr = get_attr; + gv->set_attr = set_attr; + gv->next = v->vars; + } + } + v->vars = gv; + } + + SWIGINTERN PyObject * + SWIG_globals(void) { + static PyObject *_SWIG_globals = 0; + if (!_SWIG_globals) _SWIG_globals = SWIG_newvarlink(); + return _SWIG_globals; + } + + /* ----------------------------------------------------------------------------- + * constants/methods manipulation + * ----------------------------------------------------------------------------- */ + + /* Install Constants */ + SWIGINTERN void + SWIG_Python_InstallConstants(PyObject *d, swig_const_info constants[]) { + PyObject *obj = 0; + size_t i; + for (i = 0; constants[i].type; ++i) { + switch(constants[i].type) { + case SWIG_PY_POINTER: + obj = SWIG_InternalNewPointerObj(constants[i].pvalue, *(constants[i]).ptype,0); + break; + case SWIG_PY_BINARY: + obj = SWIG_NewPackedObj(constants[i].pvalue, constants[i].lvalue, *(constants[i].ptype)); + break; + default: + obj = 0; + break; + } + if (obj) { + PyDict_SetItemString(d, constants[i].name, obj); + Py_DECREF(obj); + } + } + } + + /* -----------------------------------------------------------------------------*/ + /* Fix SwigMethods to carry the callback ptrs when needed */ + /* -----------------------------------------------------------------------------*/ + + SWIGINTERN void + SWIG_Python_FixMethods(PyMethodDef *methods, + swig_const_info *const_table, + swig_type_info **types, + swig_type_info **types_initial) { + size_t i; + for (i = 0; methods[i].ml_name; ++i) { + const char *c = methods[i].ml_doc; + if (!c) continue; + c = strstr(c, "swig_ptr: "); + if (c) { + int j; + swig_const_info *ci = 0; + const char *name = c + 10; + for (j = 0; const_table[j].type; ++j) { + if (strncmp(const_table[j].name, name, + strlen(const_table[j].name)) == 0) { + ci = &(const_table[j]); + break; + } + } + if (ci) { + void *ptr = (ci->type == SWIG_PY_POINTER) ? ci->pvalue : 0; + if (ptr) { + size_t shift = (ci->ptype) - types; + swig_type_info *ty = types_initial[shift]; + size_t ldoc = (c - methods[i].ml_doc); + size_t lptr = strlen(ty->name)+2*sizeof(void*)+2; + char *ndoc = (char*)malloc(ldoc + lptr + 10); + if (ndoc) { + char *buff = ndoc; + strncpy(buff, methods[i].ml_doc, ldoc); + buff += ldoc; + strncpy(buff, "swig_ptr: ", 10); + buff += 10; + SWIG_PackVoidPtr(buff, ptr, ty->name, lptr); + methods[i].ml_doc = ndoc; + } + } + } + } + } + } + +#ifdef __cplusplus +} +#endif + +/* -----------------------------------------------------------------------------* + * Partial Init method + * -----------------------------------------------------------------------------*/ + +#ifdef __cplusplus +extern "C" +#endif + +SWIGEXPORT +#if PY_VERSION_HEX >= 0x03000000 +PyObject* +#else +void +#endif +SWIG_init(void) { + PyObject *m, *d, *md; +#if PY_VERSION_HEX >= 0x03000000 + static struct PyModuleDef SWIG_module = { +# if PY_VERSION_HEX >= 0x03020000 + PyModuleDef_HEAD_INIT, +# else + { + PyObject_HEAD_INIT(NULL) + NULL, /* m_init */ + 0, /* m_index */ + NULL, /* m_copy */ + }, +# endif + (char *) SWIG_name, + NULL, + -1, + SwigMethods, + NULL, + NULL, + NULL, + NULL + }; +#endif + +#if defined(SWIGPYTHON_BUILTIN) + static SwigPyClientData SwigPyObject_clientdata = { + 0, 0, 0, 0, 0, 0, 0 + }; + static PyGetSetDef this_getset_def = { + (char *)"this", &SwigPyBuiltin_ThisClosure, NULL, NULL, NULL + }; + static SwigPyGetSet thisown_getset_closure = { + (PyCFunction) SwigPyObject_own, + (PyCFunction) SwigPyObject_own + }; + static PyGetSetDef thisown_getset_def = { + (char *)"thisown", SwigPyBuiltin_GetterClosure, SwigPyBuiltin_SetterClosure, NULL, &thisown_getset_closure + }; + PyObject *metatype_args; + PyTypeObject *builtin_pytype; + int builtin_base_count; + swig_type_info *builtin_basetype; + PyObject *tuple; + PyGetSetDescrObject *static_getset; + PyTypeObject *metatype; + SwigPyClientData *cd; + PyObject *public_interface, *public_symbol; + PyObject *this_descr; + PyObject *thisown_descr; + PyObject *self = 0; + int i; + + (void)builtin_pytype; + (void)builtin_base_count; + (void)builtin_basetype; + (void)tuple; + (void)static_getset; + (void)self; + + /* metatype is used to implement static member variables. */ + metatype_args = Py_BuildValue("(s(O){})", "SwigPyObjectType", &PyType_Type); + assert(metatype_args); + metatype = (PyTypeObject *) PyType_Type.tp_call((PyObject *) &PyType_Type, metatype_args, NULL); + assert(metatype); + Py_DECREF(metatype_args); + metatype->tp_setattro = (setattrofunc) &SwigPyObjectType_setattro; + assert(PyType_Ready(metatype) >= 0); +#endif + + /* Fix SwigMethods to carry the callback ptrs when needed */ + SWIG_Python_FixMethods(SwigMethods, swig_const_table, swig_types, swig_type_initial); + +#if PY_VERSION_HEX >= 0x03000000 + m = PyModule_Create(&SWIG_module); +#else + m = Py_InitModule((char *) SWIG_name, SwigMethods); +#endif + + md = d = PyModule_GetDict(m); + (void)md; + + SWIG_InitializeModule(0); + +#ifdef SWIGPYTHON_BUILTIN + SwigPyObject_stype = SWIG_MangledTypeQuery("_p_SwigPyObject"); + assert(SwigPyObject_stype); + cd = (SwigPyClientData*) SwigPyObject_stype->clientdata; + if (!cd) { + SwigPyObject_stype->clientdata = &SwigPyObject_clientdata; + SwigPyObject_clientdata.pytype = SwigPyObject_TypeOnce(); + } else if (SwigPyObject_TypeOnce()->tp_basicsize != cd->pytype->tp_basicsize) { + PyErr_SetString(PyExc_RuntimeError, "Import error: attempted to load two incompatible swig-generated modules."); +# if PY_VERSION_HEX >= 0x03000000 + return NULL; +# else + return; +# endif + } + + /* All objects have a 'this' attribute */ + this_descr = PyDescr_NewGetSet(SwigPyObject_type(), &this_getset_def); + (void)this_descr; + + /* All objects have a 'thisown' attribute */ + thisown_descr = PyDescr_NewGetSet(SwigPyObject_type(), &thisown_getset_def); + (void)thisown_descr; + + public_interface = PyList_New(0); + public_symbol = 0; + (void)public_symbol; + + PyDict_SetItemString(md, "__all__", public_interface); + Py_DECREF(public_interface); + for (i = 0; SwigMethods[i].ml_name != NULL; ++i) + SwigPyBuiltin_AddPublicSymbol(public_interface, SwigMethods[i].ml_name); + for (i = 0; swig_const_table[i].name != 0; ++i) + SwigPyBuiltin_AddPublicSymbol(public_interface, swig_const_table[i].name); +#endif + + SWIG_InstallConstants(d,swig_const_table); + +#if PY_VERSION_HEX >= 0x03000000 + return m; +#else + return; +#endif +} + diff --git a/utils/setup.py b/utils/setup.py new file mode 100644 index 00000000..329d11de --- /dev/null +++ b/utils/setup.py @@ -0,0 +1,16 @@ +""" + setup.py file for SWIG example +""" +from distutils.core import setup, Extension +import numpy + +polyiou_module = Extension('_polyiou', + sources=['polyiou_wrap.cxx', 'polyiou.cpp'], + ) +setup(name = 'polyiou', + version = '0.1', + author = "SWIG Docs", + description = """Simple swig example from docs""", + ext_modules = [polyiou_module], + py_modules = ["polyiou"], +) diff --git a/utils/torch_utils.py b/utils/torch_utils.py new file mode 100644 index 00000000..8c00f244 --- /dev/null +++ b/utils/torch_utils.py @@ -0,0 +1,239 @@ +import logging +import math +import os +import time +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_torch_seeds(seed=0): + torch.manual_seed(seed) + + # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html + 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='', batch_size=None): + ''' + device = 'cpu' or '0' or '0,1,2,3' + return ： + torch.device('cuda:0' if cuda else 'cpu') + ''' + cpu_request = device.lower() == 'cpu' + if device and not cpu_request: # if device requested other than 'cpu' + os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable + assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device # check availablity + + cuda = False if cpu_request else torch.cuda.is_available() + if cuda: + c = 1024 ** 2 # bytes to MB + ng = torch.cuda.device_count() + 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 ' + for i in range(0, ng): + if i == 1: + s = ' ' * len(s) + logger.info("%sdevice%g _CudaDeviceProperties(name='%s', total_memory=%dMB)" % + (s, i, x[i].name, x[i].total_memory / c)) + else: + logger.info('Using CPU') + + logger.info('') # skip a line + return torch.device('cuda:0' if cuda else 'cpu') + + +def time_synchronized(): + torch.cuda.synchronize() if torch.cuda.is_available() else None + return time.time() + + +def is_parallel(model): + return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) + + +def intersect_dicts(da, db, exclude=()): + ''' + 匹配keys和shapes的dicts交集，省略“exclude”键，采用da值 + 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): + ''' + 初始化model模型中的所有层中使用到的权重与相关参数 + ''' + for m in model.modules(): + t = type(m) + if t is nn.Conv2d: + pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif t is nn.BatchNorm2d: + m.eps = 1e-3 + m.momentum = 0.03 + elif t in [nn.LeakyReLU, nn.ReLU, nn.ReLU6]: + m.inplace = True + + +def find_modules(model, mclass=nn.Conv2d): + # 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): + # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/ + + # init + 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 spatial bias + b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias + b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps)) + fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn) + + return fusedconv + + +def model_info(model, verbose=False): + # Plots a line-by-line description of a PyTorch model + n_p = sum(x.numel() for x in model.parameters()) # number parameters + n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients + if verbose: + print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma')) + for i, (name, p) in enumerate(model.named_parameters()): + name = name.replace('module_list.', '') + print('%5g %40s %9s %12g %20s %10.3g %10.3g' % + (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) + + try: # FLOPS + from thop import profile + flops = profile(deepcopy(model), inputs=(torch.zeros(1, 3, 64, 64),), verbose=False)[0] / 1E9 * 2 + fs = ', %.1f GFLOPS' % (flops * 100) # 640x640 FLOPS + except: + fs = '' + + logger.info( + 'Model Summary: %g layers, %g parameters, %g gradients%s' % (len(list(model.parameters())), n_p, n_g, fs)) + + +def load_classifier(name='resnet101', n=2): + # Loads a pretrained model reshaped to n-class output + model = torchvision.models.__dict__[name](pretrained=True) + + # 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.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=False): # img(16,3,256,416), r=ratio + # scales img(bs,3,y,x) by ratio + 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: + """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models + Keep a moving average of everything in the model state_dict (parameters and buffers). + 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. + This class is sensitive where it is initialized in the sequence of model init, + GPU assignment and distributed training wrappers. + """ + + 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) + for p in self.ema.parameters(): + p.requires_grad_(False) + + def update(self, model): + # Update EMA parameters + with torch.no_grad(): + self.updates += 1 + d = self.decay(self.updates) + + 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, include=(), exclude=('process_group', 'reducer')): + # Update EMA attributes + copy_attr(self.ema, model, include, exclude) diff --git "a/weights/\346\235\203\351\207\215\346\226\207\344\273\266\350\256\260\345\276\227\344\270\213\350\275\275\345\223\237.txt" "b/weights/\346\235\203\351\207\215\346\226\207\344\273\266\350\256\260\345\276\227\344\270\213\350\275\275\345\223\237.txt" new file mode 100644 index 00000000..e69de29b