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Repository: KevinMuyaoGuo/yolov5s_for_satellite_imagery
Branch: master
Commit: c384516e6843
Files: 33
Total size: 3.4 MB
Directory structure:
gitextract_an16xrke/
├── .dockerignore
├── .gitattributes
├── .gitignore
├── Dockerfile
├── LICENSE
├── README.md
├── detect.py
├── hubconf.py
├── models/
│ ├── __init__.py
│ ├── common.py
│ ├── experimental.py
│ ├── export.py
│ ├── hub/
│ │ ├── yolov3-spp.yaml
│ │ ├── yolov5-fpn.yaml
│ │ └── yolov5-panet.yaml
│ ├── yolo.py
│ ├── yolov5l.yaml
│ ├── yolov5m.yaml
│ ├── yolov5s.yaml
│ └── yolov5x.yaml
├── requirements.txt
├── sotabench.py
├── test.py
├── train.py
├── tutorial.ipynb
├── utils/
│ ├── __init__.py
│ ├── activations.py
│ ├── datasets.py
│ ├── evolve.sh
│ ├── general.py
│ ├── google_utils.py
│ └── torch_utils.py
└── weights/
└── download_weights.sh
================================================
FILE CONTENTS
================================================
================================================
FILE: .dockerignore
================================================
# 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
================================================
FILE: .gitattributes
================================================
# this drop notebooks from GitHub language stats
*.ipynb linguist-vendored
================================================
FILE: .gitignore
================================================
# Repo-specific GitIgnore ----------------------------------------------------------------------------------------------
*.jpg
*.jpeg
*.png
*.bmp
*.tif
*.tiff
*.heic
*.JPG
*.JPEG
*.PNG
*.BMP
*.TIF
*.TIFF
*.HEIC
*.mp4
*.mov
*.MOV
*.avi
*.data
*.json
*.cfg
!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
================================================
FILE: Dockerfile
================================================
# Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
FROM nvcr.io/nvidia/pytorch:20.08-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
================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# 基于 YOLOv5 的卫星图像目标检测
## 1 数据准备
### 1.1 数据集采集
本项目使用 DOTA 数据集，数据集链接：[下载地址](https://captain-whu.github.io/DOTA/dataset.html)
- 原数据集中待检测的目标
<table>
<tr>
<td>
<center><img src="./README_figures/class_info.png" align="center" style="width: 80px;"></center>
<center><font size=2>图1-1 目标类别</font></center>
</td>
</tr>
<tr>
<td>
<center><img src="./README_figures/16_classes.png" align="center" style="width: 550px;"></center>
<center><font size=2>图1-2 待检测目标一览</font></center>
</td>
</tr>
</table>
- 原数据集中的图像
由 图1-3 可以看出原数据集中的图像尺寸和分辨率大小不一。
<table>
<tr>
<td>
<center><img src="./README_figures/original_image.png" align="center" style="width: 380px;"></center>
<center><font size=2>图1-3 数据集图像一览</font></center>
</td>
</tr>
</table>
- 原数据集中的标签
标签文件内容如 图1-4 所示。
<table>
<tr>
<td>
<center><img src="./README_figures/DOTA_format_label.png" align="center" style="width: 600px;"></center>
<center><font size=2>图1-4 数据集标签一览</font></center>
</td>
</tr>
</table>
标签格式如下：
```
'imagesource':<imagesource>
'gsd':<gsd>
<x1> <y1> <x2> <y2> <x3> <y3> <x4> <y4> <class> <difficulty>
```
其中，
- 1，2，3，4 分别为标记目标的四个点
- `x`，`y` 为标记点的坐标值
- `class` 为目标类别
- `difficulty` 为检测难度（ 0/1：简单/困难 ）
### 1.2 标签格式转换和标签分布统计
YOLO 模型的标签输入有着特定的格式：
```
<class> <x_center> <y_center> <width> <height>
```
其中，
- `class` 为目标类别
- `x_center` 为标记框中心点的 x 坐标与图片宽度的比值
- `y_center` 为标记框中心点的 y 坐标与图片高度的比值
- `width` 为标记框的宽度与图片宽度的比值
- `height` 为标记框的高度与图片高度的比值
而原数据集中的标签数据格式和 YOLO 模型要求输入的标签数据格式不一致，因此我们将 DOTA 标签数据格式批量转换成 YOLO 标签数据格式，转换格式后标签文件内容如 图1-5 所示。
<table>
<tr>
<td>
<center><img src="./README_figures/YOLO_format_label.png" align="center" style="width: 550px;"></center>
<center><font size=2>图1-5 格式转换后的数据集标签一览</font></center>
</td>
</tr>
</table>
格式转换后，我们统计了标签中各个参数的分布。
- 目标的数量分布
<table>
<tr>
<td>
<center><img src="./README_figures/obj_class_dist.png" align="center" style="width: 300px;"></center>
<center><font size=2>图1-6 各个类别目标分布数量的柱状图</font></center>
</td>
</tr>
</table>
- 目标的尺寸分布
<table>
<tr>
<td>
<center><img src="./README_figures/obj_size_dist.png" align="center" style="width: 300px;"></center>
<center><font size=2>图1-7 目标尺寸分布的散点热力图</font></center>
</td>
</tr>
</table>
- 目标的位置分布
<table>
<tr>
<td>
<center><img src="./README_figures/obj_loc_dist.png" align="center" style="width: 300px;"></center>
<center><font size=2>图1-8 目标位置分布的散点热力图</font></center>
</td>
</tr>
</table>
### 1.3 图像分割和尺寸调整
YOLO 模型的图像输入尺寸是固定的，由于原数据集中的图像尺寸不一，我们将原数据集中的图像按目标分布的位置分割成一个个包含目标的子图，并将每个子图尺寸调整为 1024×1024。分割前的图像如 图1-9 所示，分割后的子图效果如 图1-10 所示。
<table>
<tr>
<td>
<center><img src="./README_figures/full_img.png" align="center" style="width: 450px;"></center>
<center><font size=2>图1-9 分割前的图像</font></center>
</td>
</tr>
<tr>
<td>
<center><img src="./README_figures/split_img.png" align="center" style="width: 550px;"></center>
<center><font size=2>图1-10 分割后的效果</font></center>
</td>
</tr>
</table>
### 1.4 数据集目录结构调整
YOLOv5 在读取数据时使用特定的目录结构，调整后的数据集目录结构如 图1-11 所示，各个目录中的内容如 图1-12 ~ 图1-15 所示。
<table>
<tr>
<td>
<center><img src="./README_figures/dataset_dir.png" align="center" style="width: 200px;"></center>
<center><font size=2>图1-11 输入数据目录结构</font></center>
</td>
</tr>
<tr>
<td>
<center><img src="./README_figures/image_train_dir.png" align="center" style="width: 250px;"></center>
<center><font size=2>图1-12 训练集图片目录内容</font></center>
</td>
<td>
<center><img src="./README_figures/image_val_dir.png" align="center" style="width: 220px;"></center>
<center><font size=2>图1-13 验证集图片目录内容</font></center>
</td>
</tr>
<tr>
<td>
<center><img src="./README_figures/label_train_dir.png" align="center" style="width: 230px;"></center>
<center><font size=2>图1-14 训练集标签目录内容</font></center>
</td>
<td>
<center><img src="./README_figures/label_val_dir.png" align="center" style="width: 200px;"></center>
<center><font size=2>图1-15 验证集标签目录内容</font></center>
</td>
</tr>
</table>
## 2 模型训练
### 2.1 环境搭建
我们使用远程 GPU 服务器（MistGPU）训练模型，服务器硬件配置如 图2-1 所示。
<table>
<tr>
<td>
<center><img src="./README_figures/server_config.png" align="center" style="width: 160px;"></center>
<center><font size=2>图2-1 GPU 服务器硬件配置</font></center>
</td>
</tr>
</table>
服务器软件配置：
- 操作系统：Ubuntu 18.04.4
- 开发和运行环境：
- PyTorch 版本：1.6.0
- CUDA 版本：10.2
本地连接服务器的工具：FinalShell 1.0
首先我们在本地修改工程中相关配置文件并测试跑通：
- 在 `data/` 目录中创建 `DOTA.yaml` 配置文件，并设置自定义的数据集路径，如 图2-2所示
- 修改 `yolov5s(/*m/*l/*x).yaml` 中的 `nc` 参数值为自定义目标类别数
<table>
<tr>
<td>
<center><img src="./README_figures/DOTA_yaml.png" align="center" style="width: 450px;"></center>
<center><font size=2>图2-2 自定义数据集路径</font></center>
</td>
</tr>
</table>
然后我们将在本地调试好的 YOLOv5 工程打包压缩，将预处理好的数据集打包压缩，将两者上传至服务器。上传后分别用 unzip 命令解压，注意 YOLOv5 工程目录和数据集根目录要在同一级目录下，如 图2-3 所示。
<table>
<tr>
<td>
<center><img src="./README_figures/server_dir.png" align="center" style="width: 350px;"></center>
<center><font size=2>图2-3 数据集和YOLOv5目录的位置</font></center>
</td>
</tr>
</table>
进入 `yolov5/` 目录，使用以下命令安装 YOLOv5 工程所需的配置文件：
```
pip install -U -r requirements.txt
```
### 2.2 训练
在 `yolov5/` 目录，运行 `train.py` 文件开始训练：
```
screen python3 train.py --weight weights/yolov5s.pt --batch 16 --epochs 100 --cache
```
其中的参数说明：
- `--weight`：使用的预训练权重，这里示范使用的是 yolov5s 模型的预训练权重
- `--batch`：mini-batch 的大小，这里使用 16
- `--epochs`：训练的迭代次数，这里我们训练 100 个 epoch
- `--cache`：使用数据缓存，加速训练进程
另：命令开头使用的 `screen` 是 linux 的窗口管理器，可以使训练过程在后台运行，当网络不稳定导致服务器连接中断时，可以使用 `screen -r` 命令重新进入训练界面。
训练开始时的日志信息如 图2-4 所示，包括配置信息、网络架构等。
<table>
<tr>
<td>
<center><img src="./README_figures/train_log.png" align="center" style="width: 800px;"></center>
<center><font size=2>图2-4 训练开始日志信息</font></center>
</td>
</tr>
</table>
训练过程的前3个 epoch 如 图2-5 所示。
<table>
<tr>
<td>
<center><img src="./README_figures/train_shot.png" align="center" style="width: 800px;"></center>
<center><font size=2>图2-5 训练过程日志信息</font></center>
</td>
</tr>
</table>
其中，
- `Epoch`：迭代数
- `gpu_mem`：使用的显存大小
- `GIoU`：GIoU 损失函数均值，此处打印的值是 L_GIoU=1-GIoU
- `obj`：目标检测的损失函数均值
- `cls`：目标分类的损失函数均值
- `total`：【暂不清楚】
- `targets`：【暂不清楚】
- `img_size`：图片尺寸（分辨率）
- `Class`：验证的目标类别
- `Images`：图片总数
- `Targets`：目标总数
- `P`：准确率【TP / (TP + FP)，即“找对的/找到的”】
- `R`：召回率【TP / (TP + FN)，即“找对的/该找对的”】
- `mAP@.5`：AP 是以 Precision 和 Recall 为两轴作图后曲线围成的面积，m 表示平均，@ 后面的数表示判定IoU 为正负样本的阈值
- `mAP@.5:.95`：表示在不同 GIoU 阈值（从0.5到0.95，步长0.05，即：0.5、0.55、0.6、0.65、0.7、0.75、0.8、0.85、0.9、0.95）上的平均 mAP
另：
- GIoU（Generalized Intersection over Union）定义如 图2-6 所示；IoU 定义如 图2-7 所示。
<table>
<tr>
<td>
<center><img src="./README_figures/GIoU.png" align="center" style="width: 540px;"></center>
<center><font size=2>图2-6 GIoU 定义</font></center>
</td>
</tr>
<tr>
<td>
<center><img src="./README_figures/IoU.png" align="center" style="width: 400px;"></center>
<center><font size=2>图2-7 IoU 定义</font></center>
</td>
</tr>
</table>
- mAP（mean Average Precision）定义：
- mAP: mean Average Precision, 即各类别AP的平均值
- AP: PR 曲线下面积
- PR曲线: Precision-Recall 曲线
- Precision: TP / (TP + FP)
- Recall: TP / (TP + FN)
- TP: IoU>0.5 的检测框数量（同一Ground Truth只计算一次）
- FP: IoU<=0.5 的检测框数量，或者是检测到同一个 Ground Truth 的多余检测框的数量
- FN: 没有检测到的 Ground Truth 的数量
附：mAP 讲解：[理解目标检测当中的mAP](https://blog.csdn.net/hsqyc/article/details/81702437?utm_medium=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.nonecase&depth_1-utm_source=distribute.pc_relevant_t0.none-task-blog-BlogCommendFromMachineLearnPai2-1.nonecase)
训练一个 epoch 后可以在 `runs/` 目录下看到改 epoch 各个训练 batch 的在图上的标签，如图 2-8 所示。
<table>
<tr>
<td>
<center><img src="./README_figures/train_batch_labels.jpg" align="center" style="width:600px;"></center>
<center><font size=2>图2-8 训练 batch 标签</font></center>
</td>
</tr>
</table>
### 2.3 训练和验证的结果
我们使用上述方法和数据集分别在基于 COCO 数据集的预训练模型 yolov5s 和 yolov5m 的基础上训练了自己的权重。yolov5s 模型训练用时 10 小时左右，yolov5m 模型训练用时 20 小时左右。
我们记录了每一个 epoch 的训练过程和验证过程的指标值，并绘制了评价指标曲线。yolov5s 模型的评价指标曲线如 图2-9 所示，yolov5m 评价指标模型的曲线如 图2-10 所示。
<table>
<tr>
<td>
<center><img src="./README_figures/s_results.png" align="center" style="width: 700px;"></center>
<center><font size=2>图2-9 yolov5s 模型评价指标曲线</font></center>
</td>
</tr>
<tr>
<td>
<center><img src="./README_figures/m_results.png" align="center" style="width: 700px;"></center>
<center><font size=2>图2-10 yolov5m 模型评价指标曲线</font></center>
</td>
</tr>
</table>
其中，
- GIoU：训练集 GIoU 损失函数均值；该值越小，检测方框的精确度越高
- val GIoU：验证集 GIoU 损失函数均值；该值越小，检测方框的精确度越高
- Objectness：训练集目标检测损失函数均值；该值越小，目标检测的准确率越高
- val Objectness：验证集目标检测损失函数均值；该值越小，目标检测的准确率越高
- Classification：训练集目标分类的损失函数均值；该值越小，目标分类准确度越高
- val Classification：验证集目标分类的损失函数均值；该值越小，目标分类准确度越高
- Precision：准确率
- Recall：召回率
- mAP@0.5：GIoU 阈值为 0.5 的平均 AP 值
- mAP@0.5:0.95：在不同 IoU 阈值（从0.5到0.95，步长0.05）上的平均 mAP
由结果曲线可以看出，yolov5s 和 yolov5m 都取得了很好的结果，大部分曲线随着 epoch 数增加都趋于收敛。我们有以下结论：
1. yolov5m 的验证集目标检测损失函数值在 50 个 epoch 后有轻微的上升的趋势，说明在验证集上存在轻微的过拟合问题
2. yolov5s 和 yolov5m 在目标分类上都有着很好的效果
3. 同等迭代次数训练后，yolov5m 的准确率会超过 0.6，高于 yolov5s 的准确率
4. yolov5s 和 yolov5m 的 mAP 收敛值相差很小，但 yolov5m 会高于 yolov5s，即 yolov5m 的效果会稍好于 yolov5s
## 3 模型测试
### 3.1 模型效果测试
我们分别对训练好的 yolov5s 和 yolov5m 模型做卫星图像目标检测测试，测试结果如 图3-1 ~ 图3-4 所示。
<table>
<tr>
<td>
<center><img src="./README_figures/s_test_res1.png" align="center" style="width: 350px;"></center>
<center><font size=2>图3-1-a yolov5s 测试结果1</font></center>
</td>
<td>
<center><img src="./README_figures/m_test_res1.png" align="center" style="width: 350px;"></center>
<center><font size=2>图3-1-b yolov5m 测试结果1</font></center>
</td>
</tr>
<tr>
<td>
<center><img src="./README_figures/s_test_res2.png" align="center" style="width: 350px;"></center>
<center><font size=2>图3-2-a yolov5s 测试结果2</font></center>
</td>
<td>
<center><img src="./README_figures/m_test_res2.png" align="center" style="width: 350px;"></center>
<center><font size=2>图3-2-b yolov5m 测试结果2</font></center>
</td>
</tr>
<tr>
<td>
<center><img src="./README_figures/s_test_res3.png" align="center" style="width: 350px;"></center>
<center><font size=2>图3-3-a yolov5s 测试结果3</font></center>
</td>
<td>
<center><img src="./README_figures/m_test_res3.png" align="center" style="width: 350px;"></center>
<center><font size=2>图3-3-b yolov5m 测试结果3</font></center>
</td>
</tr>
<tr>
<td>
<center><img src="./README_figures/s_test_res4.png" align="center" style="width: 350px;"></center>
<center><font size=2>图3-4-a yolov5s 测试结果4</font></center>
</td>
<td>
<center><img src="./README_figures/m_test_res4.png" align="center" style="width: 350px;"></center>
<center><font size=2>图3-4-b yolov5m 测试结果4</font></center>
</td>
</tr>
</table>
yolov5s 和 yolov5m 模型的每张图片推断用时如 图3-5 和 图3-6 所示。
<table>
<tr>
<td>
<center><img src="./README_figures/s_infer_shot.png" align="center" style="width: 700px;"></center>
<center><font size=2>图3-5 yolov5s 模型推断用时</font></center>
</td>
</tr>
<tr>
<td>
<center><img src="./README_figures/m_infer_shot.png" align="center" style="width: 700px;"></center>
<center><font size=2>图3-6 yolov5m 模型推断用时</font></center>
</td>
</tr>
</table>
### 3.2 模型效果总结
总的来说，yolov5s 和 yolov5m 模型效果相差不大，都能检测到给定图像中的大部分目标并正确分类。
模型效果总结：
- yolov5m 在给定图像的目标检测数量上强于 yolov5s
- yolov5m 极少数情况下会将图像中个别目标错误分类，yolov5s 这种情况较为少见
- yolov5s 和 yolov5m 都能准确地框住目标，且两者都能准确地检测到图像边缘的不完整目标
- yolov5s 和 yolov5m 在图像分辨率较低或者目标有半透明云层遮挡情况下都能检测到目标
- yolov5s 比 yolov5m 推断用时少大约一倍
## 4 未来工作
日后将考虑训练 yolov5l 以及 yolov5x 模型，增加所有模型的训练 epoch 数到 300，并对比各个模型的优劣。
================================================
FILE: detect.py
================================================
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_coords,
xyxy2xywh, plot_one_box, strip_optimizer, set_logging)
from utils.torch_utils import select_device, load_classifier, time_synchronized
def detect(save_img=False):
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') or source.startswith('http') or source.endswith('.txt')
# Initialize
set_logging()
device = select_device(opt.device)
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
if half:
model.half() # to FP16
# Second-stage classifier
classify = False
if classify:
modelc = 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
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
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
# Run inference
t0 = time.time()
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
for path, img, im0s, vid_cap in dataset:
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
t2 = time_synchronized()
# Apply Classifier
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# Process detections
for i, det in enumerate(pred): # detections per image
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)
txt_path = str(Path(out) / Path(p).stem) + ('_%g' % dataset.frame if dataset.mode == 'video' else '')
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if det is not None and len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += '%g %ss, ' % (n, names[int(c)]) # add to string
# Write results
for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
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)
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
# Print time (inference + NMS)
print('%sDone. (%.3fs)' % (s, t2 - t1))
# Stream results
if view_img:
cv2.imshow(p, im0)
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)
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))
if platform.system() == 'Darwin' and not opt.update: # MacOS
os.system('open ' + save_path)
print('Done. (%.3fs)' % (time.time() - t0))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='inference/images', help='source') # file/folder, 0 for webcam
parser.add_argument('--output', type=str, default='inference/output', help='output folder') # output folder
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.4, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='display results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--update', action='store_true', help='update all models')
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()
strip_optimizer(opt.weights)
else:
detect()
================================================
FILE: hubconf.py
================================================
"""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.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
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)
================================================
FILE: models/__init__.py
================================================
================================================
FILE: models/common.py
================================================
# This file contains modules common to various models
import math
import torch
import torch.nn as nn
def autopad(k, p=None): # kernel, padding
# Pad to 'same'
if p is None:
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
return p
def DWConv(c1, c2, k=1, s=1, act=True):
# Depthwise convolution
return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
class Conv(nn.Module):
# Standard convolution
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
super(Conv, self).__init__()
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
self.bn = nn.BatchNorm2d(c2)
self.act = nn.Hardswish() if act else nn.Identity()
def forward(self, x):
return self.act(self.bn(self.conv(x)))
def fuseforward(self, x):
return self.act(self.conv(x))
class Bottleneck(nn.Module):
# Standard bottleneck
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
super(Bottleneck, self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_, c2, 3, 1, g=g)
self.add = shortcut and c1 == c2
def forward(self, x):
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
class BottleneckCSP(nn.Module):
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
super(BottleneckCSP, self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
self.cv4 = Conv(2 * c_, c2, 1, 1)
self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
self.act = nn.LeakyReLU(0.1, inplace=True)
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
def forward(self, x):
y1 = self.cv3(self.m(self.cv1(x)))
y2 = self.cv2(x)
return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
class SPP(nn.Module):
# Spatial pyramid pooling layer used in YOLOv3-SPP
def __init__(self, c1, c2, k=(5, 9, 13)):
super(SPP, self).__init__()
c_ = c1 // 2 # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
def forward(self, x):
x = self.cv1(x)
return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
class Focus(nn.Module):
# Focus wh information into c-space
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
super(Focus, self).__init__()
self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
class Concat(nn.Module):
# Concatenate a list of tensors along dimension
def __init__(self, dimension=1):
super(Concat, self).__init__()
self.d = dimension
def forward(self, x):
return torch.cat(x, self.d)
class Flatten(nn.Module):
# Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions
@staticmethod
def forward(x):
return x.view(x.size(0), -1)
class Classify(nn.Module):
# Classification head, i.e. x(b,c1,20,20) to x(b,c2)
def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
super(Classify, self).__init__()
self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) # to x(b,c2,1,1)
self.flat = Flatten()
def forward(self, x):
z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
return self.flat(self.conv(z)) # flatten to x(b,c2)
================================================
FILE: models/experimental.py
================================================
# This file contains experimental modules
import numpy as np
import torch
import torch.nn as nn
from models.common import Conv, DWConv
from utils.google_utils import attempt_download
class CrossConv(nn.Module):
# Cross Convolution Downsample
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
# ch_in, ch_out, kernel, stride, groups, expansion, shortcut
super(CrossConv, self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, (1, k), (1, s))
self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
self.add = shortcut and c1 == c2
def forward(self, x):
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
class C3(nn.Module):
# Cross Convolution CSP
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
super(C3, self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
self.cv4 = Conv(2 * c_, c2, 1, 1)
self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
self.act = nn.LeakyReLU(0.1, inplace=True)
self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
def forward(self, x):
y1 = self.cv3(self.m(self.cv1(x)))
y2 = self.cv2(x)
return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
class Sum(nn.Module):
# Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
def __init__(self, n, weight=False): # n: number of inputs
super(Sum, self).__init__()
self.weight = weight # apply weights boolean
self.iter = range(n - 1) # iter object
if weight:
self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True) # layer weights
def forward(self, x):
y = x[0] # no weight
if self.weight:
w = torch.sigmoid(self.w) * 2
for i in self.iter:
y = y + x[i + 1] * w[i]
else:
for i in self.iter:
y = y + x[i + 1]
return y
class GhostConv(nn.Module):
# Ghost Convolution https://github.com/huawei-noah/ghostnet
def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
super(GhostConv, self).__init__()
c_ = c2 // 2 # hidden channels
self.cv1 = Conv(c1, c_, k, s, 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):
# Ensemble of models
def __init__(self):
super(Ensemble, self).__init__()
def forward(self, x, augment=False):
y = []
for module in self:
y.append(module(x, augment)[0])
# y = torch.stack(y).max(0)[0] # max ensemble
# y = torch.cat(y, 1) # nms ensemble
y = torch.stack(y).mean(0) # mean ensemble
return y, None # inference, train output
def attempt_load(weights, map_location=None):
# Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
model = Ensemble()
for w in weights if isinstance(weights, list) else [weights]:
attempt_download(w)
model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval()) # load FP32 model
if len(model) == 1:
return model[-1] # return model
else:
print('Ensemble created with %s\n' % weights)
for k in ['names', 'stride']:
setattr(model, k, getattr(model[-1], k))
return model # return ensemble
================================================
FILE: models/export.py
================================================
"""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 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
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()
# Input
img = torch.zeros((opt.batch_size, 3, *opt.img_size)) # image size(1,3,320,192) iDetection
# Load PyTorch model
model = attempt_load(opt.weights, map_location=torch.device('cpu')) # load FP32 model
# Update model
for k, m in model.named_modules():
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatability
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='images', 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. Visualize with https://github.com/lutzroeder/netron.')
================================================
FILE: models/hub/yolov3-spp.yaml
================================================
# 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)
]
================================================
FILE: models/hub/yolov5-fpn.yaml
================================================
# 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)
]
================================================
FILE: models/hub/yolov5-panet.yaml
================================================
# 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)
]
================================================
FILE: models/yolo.py
================================================
import argparse
import logging
import math
from copy import deepcopy
from pathlib import Path
import torch
import torch.nn as nn
from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat
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)
logger = logging.getLogger(__name__)
class Detect(nn.Module):
stride = None # strides computed during build
export = False # onnx export
def __init__(self, nc=80, anchors=(), ch=()): # detection layer
super(Detect, self).__init__()
self.nc = nc # number of classes
self.no = nc + 5 # number of outputs per anchor
self.nl = len(anchors) # number of detection layers
self.na = len(anchors[0]) // 2 # number of anchors
self.grid = [torch.zeros(1)] * self.nl # init grid
a = torch.tensor(anchors).float().view(self.nl, -1, 2)
self.register_buffer('anchors', a) # shape(nl,na,2)
self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2)
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
def forward(self, x):
# x = x.copy() # for profiling
z = [] # inference output
self.training |= self.export
for i in range(self.nl):
x[i] = self.m[i](x[i]) # conv
bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
if not self.training: # inference
if self.grid[i].shape[2:4] != x[i].shape[2:4]:
self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
y = x[i].sigmoid()
y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy
y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
z.append(y.view(bs, -1, self.no))
return x if self.training else (torch.cat(z, 1), x)
@staticmethod
def _make_grid(nx=20, ny=20):
yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
class Model(nn.Module):
def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None): # model, input channels, number of classes
super(Model, self).__init__()
if isinstance(cfg, dict):
self.yaml = cfg # model dict
else: # is *.yaml
import yaml # for torch hub
self.yaml_file = Path(cfg).name
with open(cfg) as f:
self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict
# Define model
if nc and nc != self.yaml['nc']:
print('Overriding %s nc=%g with nc=%g' % (cfg, self.yaml['nc'], nc))
self.yaml['nc'] = nc # override yaml value
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist, ch_out
# print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
# Build strides, anchors
m = self.model[-1] # Detect()
if isinstance(m, Detect):
s = 128 # 2x min stride
m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
m.anchors /= m.stride.view(-1, 1, 1)
check_anchor_order(m)
self.stride = m.stride
self._initialize_biases() # only run once
# print('Strides: %s' % m.stride.tolist())
# Init weights, biases
initialize_weights(self)
self.info()
print('')
def forward(self, x, augment=False, profile=False):
if augment:
img_size = x.shape[-2:] # height, width
s = [1, 0.83, 0.67] # scales
f = [None, 3, None] # flips (2-ud, 3-lr)
y = [] # outputs
for si, fi in zip(s, f):
xi = scale_img(x.flip(fi) if fi else x, si)
yi = self.forward_once(xi)[0] # forward
# cv2.imwrite('img%g.jpg' % s, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1]) # save
yi[..., :4] /= si # de-scale
if fi == 2:
yi[..., 1] = img_size[0] - yi[..., 1] # de-flip ud
elif fi == 3:
yi[..., 0] = img_size[1] - yi[..., 0] # de-flip lr
y.append(yi)
return torch.cat(y, 1), None # augmented inference, train
else:
return self.forward_once(x, profile) # single-scale inference, train
def forward_once(self, x, profile=False):
y, dt = [], [] # outputs
for m in self.model:
if m.f != -1: # if not from previous layer
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
if profile:
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
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
print('Fusing layers... ')
for m in self.model.modules():
if type(m) is Conv:
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
m.forward = m.fuseforward # update forward
self.info()
return self
def info(self, verbose=False): # print model information
model_info(self, verbose)
def parse_model(d, ch): # model_dict, input_channels(3)
logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args
m = eval(m) if isinstance(m, str) else m # eval strings
for j, a in enumerate(args):
try:
args[j] = eval(a) if isinstance(a, str) else a # eval strings
except:
pass
n = max(round(n * gd), 1) if n > 1 else n # depth gain
if m in [nn.Conv2d, Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]:
c1, c2 = ch[f], args[0]
# Normal
# if i > 0 and args[0] != no: # channel expansion factor
# ex = 1.75 # exponential (default 2.0)
# e = math.log(c2 / ch[1]) / math.log(2)
# c2 = int(ch[1] * ex ** e)
# if m != Focus:
c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
# Experimental
# if i > 0 and args[0] != no: # channel expansion factor
# ex = 1 + gw # exponential (default 2.0)
# ch1 = 32 # ch[1]
# e = math.log(c2 / ch1) / math.log(2) # level 1-n
# c2 = int(ch1 * ex ** e)
# if m != Focus:
# c2 = make_divisible(c2, 8) if c2 != no else c2
args = [c1, c2, *args[1:]]
if m in [BottleneckCSP, C3]:
args.insert(2, n)
n = 1
elif m is nn.BatchNorm2d:
args = [ch[f]]
elif m is Concat:
c2 = sum([ch[-1 if x == -1 else x + 1] for x in f])
elif m is Detect:
args.append([ch[x + 1] for x in f])
if isinstance(args[1], int): # number of anchors
args[1] = [list(range(args[1] * 2))] * len(f)
else:
c2 = ch[f]
m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module
t = str(m)[8:-2].replace('__main__.', '') # module type
np = sum([x.numel() for x in m_.parameters()]) # number params
m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
layers.append(m_)
ch.append(c2)
return nn.Sequential(*layers), sorted(save)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', type=str, default='yolov5s.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
================================================
FILE: models/yolov5l.yaml
================================================
# parameters
nc: 16 # 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)
]
================================================
FILE: models/yolov5m.yaml
================================================
# parameters
nc: 16 # number of classes
depth_multiple: 0.67 # model depth multiple
width_multiple: 0.75 # 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)
]
================================================
FILE: models/yolov5s.yaml
================================================
# parameters
nc: 16 # 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)
]
================================================
FILE: models/yolov5x.yaml
================================================
# parameters
nc: 16 # number of classes
depth_multiple: 1.33 # model depth multiple
width_multiple: 1.25 # 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)
]
================================================
FILE: requirements.txt
================================================
# 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==4.0b3
# onnx>=1.7.0
# scikit-learn==0.19.2 # for coreml quantization
# extras --------------------------------------
# thop # FLOPS computation
# seaborn # plotting
================================================
FILE: sotabench.py
================================================
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] # GIoU, 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
================================================
FILE: test.py
================================================
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
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=False, 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 = [], [], [], []
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] # GIoU, 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()):
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)
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
# 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='weights/yolov5s.pt', help='model.pt path(s)')
parser.add_argument('--data', type=str, default='data/DOTA.yaml', help='*.data path')
parser.add_argument('--batch-size', type=int, default=16, 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='', 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('DOTA.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
================================================
FILE: train.py
================================================
import argparse
import glob
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)
from utils.google_utils import attempt_download
from utils.torch_utils import init_seeds, ModelEMA, select_device, intersect_dicts
logger = logging.getLogger(__name__)
def train(hyp, opt, device, tb_writer=None):
logger.info(f'Hyperparameters {hyp}')
log_dir = Path(tb_writer.log_dir) if tb_writer else Path(opt.logdir) / 'evolve' # logging directory
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')
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
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
cuda = device.type != 'cpu'
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
train_path = data_dict['train']
test_path = data_dict['val']
nc, names = (1, ['item']) if opt.single_cls else (int(data_dict['nc']), data_dict['names']) # number classes, names
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check
# Model
pretrained = weights.endswith('.pt')
if pretrained:
with torch_distributed_zero_first(rank):
attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) # load checkpoint
if 'anchors' in hyp and hyp['anchors']:
ckpt['model'].yaml['anchors'] = round(hyp['anchors']) # force autoanchor
model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device) # create
exclude = ['anchor'] if opt.cfg else [] # exclude keys
state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect
model.load_state_dict(state_dict, strict=False) # load
logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report
else:
model = Model(opt.cfg, ch=3, nc=nc).to(device) # create
# Freeze
freeze = ['', ] # parameter names to freeze (full or partial)
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 = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_parameters():
v.requires_grad = True
if '.bias' in k:
pg2.append(v) # biases
elif '.weight' in k and '.bn' not in k:
pg1.append(v) # apply weight decay
else:
pg0.append(v) # all else
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)
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - hyp['lrf']) + hyp['lrf'] # cosine
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)
# Resume
start_epoch, best_fitness = 0, 0.0
if pretrained:
# Optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# Results
if ckpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(ckpt['training_results']) # write results.txt
# Epochs
start_epoch = ckpt['epoch'] + 1
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
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
gs = int(max(model.stride)) # grid size (max stride)
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples
# DP mode
if cuda and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info('Using SyncBatchNorm()')
# Exponential moving average
ema = ModelEMA(model) if rank in [-1, 0] else None
# DDP mode
if cuda and rank != -1:
model = DDP(model, device_ids=[opt.local_rank], output_device=(opt.local_rank))
# Trainloader
dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank,
world_size=opt.world_size, workers=opt.workers)
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)
# Testloader
if rank in [-1, 0]:
ema.updates = start_epoch * nb // accumulate # set EMA updates
testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt,
hyp=hyp, augment=False, cache=opt.cache_images, rect=True, rank=-1,
world_size=opt.world_size, workers=opt.workers)[0] # only runs on process 0
# Model parameters
hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # giou loss ratio (obj_loss = 1.0 or giou)
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
model.names = names
# Classes and Anchors
if rank in [-1, 0] and 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)
# Start training
t0 = time.time()
nw = max(3 * 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
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
logger.info('Image sizes %g train, %g test' % (imgsz, imgsz_test))
logger.info('Using %g dataloader workers' % dataloader.num_workers)
logger.info('Starting training for %g epochs...' % epochs)
# torch.autograd.set_detect_anomaly(True)
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
if opt.image_weights:
# Generate indices
if rank in [-1, 0]:
cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 # class weights
iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
# Broadcast if DDP
if rank != -1:
indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int()
dist.broadcast(indices, 0)
if rank != 0:
dataset.indices = indices.cpu().numpy()
# Update mosaic border
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
mloss = torch.zeros(4, device=device) # mean losses
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
if rank in [-1, 0]:
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
# Warmup
if ni <= nw:
xi = [0, nw] # x interp
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # giou loss ratio (obj_loss = 1.0 or giou)
accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round())
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x['lr'] = np.interp(ni, xi, [0.1 if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
if 'momentum' in x:
x['momentum'] = np.interp(ni, xi, [0.9, hyp['momentum']])
# Multi-scale
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)
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
# Forward
with amp.autocast(enabled=cuda):
pred = model(imgs) # forward
loss, loss_items = compute_loss(pred, targets.to(device), model) # loss scaled by batch_size
if rank != -1:
loss *= opt.world_size # gradient averaged between devices in DDP mode
# Backward
scaler.scale(loss).backward()
# Optimize
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 = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 + '%10.4g' * 6) % (
'%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
pbar.set_description(s)
# Plot
if ni < 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)
# 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:
ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride'])
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
if final_epoch: # replot predictions
[os.remove(x) for x in glob.glob(str(log_dir / 'test_batch*_pred.jpg')) if os.path.exists(x)]
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)
# Write
with open(results_file, 'a') as f:
f.write(s + '%10.4g' * 7 % results + '\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
if len(opt.name) and opt.bucket:
os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
# Tensorboard
if tb_writer:
tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss', # train loss
'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
'val/giou_loss', 'val/obj_loss', 'val/cls_loss', # val loss
'x/lr0', 'x/lr1', 'x/lr2'] # params
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
tb_writer.add_scalar(tag, x, epoch)
# Update best mAP
fi = fitness(np.array(results).reshape(1, -1)) # fitness_i = weighted combination of [P, R, mAP, F1]
if fi > best_fitness:
best_fitness = fi
# Save model
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save:
with open(results_file, 'r') as f: # create checkpoint
ckpt = {'epoch': epoch,
'best_fitness': best_fitness,
'training_results': f.read(),
'model': ema.ema,
'optimizer': None if final_epoch else optimizer.state_dict()}
# 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
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
os.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket else None # upload
# Finish
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))
dist.destroy_process_group() if rank not in [-1, 0] else None
torch.cuda.empty_cache()
return results
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default='weights/yolov5s.pt', help='initial weights path')
parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml', help='model.yaml path')
parser.add_argument('--data', type=str, default='data/DOTA.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=10)
parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
parser.add_argument('--img-size', nargs='+', type=int, default=[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', help='only test final epoch')
parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
parser.add_argument('--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='', 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
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
if opt.resume: # resume an interrupted run
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'
with open(log_dir / 'opt.yaml') as f:
opt = argparse.Namespace(**yaml.load(f, Loader=yaml.FullLoader)) # replace
opt.cfg, opt.weights, opt.resume = '', ckpt, True
logger.info('Resuming training from %s' % ckpt)
else:
# opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')
opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) # check files
assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test)
log_dir = increment_dir(Path(opt.logdir) / 'exp', opt.name) # runs/exp1
device = select_device(opt.device, batch_size=opt.batch_size)
# DDP mode
if opt.local_rank != -1:
assert torch.cuda.device_count() > opt.local_rank
torch.cuda.set_device(opt.local_rank)
device = torch.device('cuda', opt.local_rank)
dist.init_process_group(backend='nccl', init_method='env://') # distributed backend
assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count'
opt.batch_size = opt.total_batch_size // opt.world_size
logger.info(opt)
with open(opt.hyp) as f:
hyp = yaml.load(f, Loader=yaml.FullLoader) # load hyps
# Train
if not opt.evolve:
tb_writer = None
if opt.global_rank in [-1, 0]:
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)
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': (0.1, 0.6, 0.98), # SGD momentum/Adam beta1
'weight_decay': (1, 0.0, 0.001), # optimizer weight decay
'giou': (1, 0.02, 0.2), # GIoU loss gain
'cls': (1, 0.2, 4.0), # cls loss gain
'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight
'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels)
'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight
'iou_t': (0, 0.1, 0.7), # IoU training threshold
'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold
'anchors': (1, 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)
'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
for _ in range(1): # generations to evolve
if os.path.exists('evolve.txt'): # if evolve.txt exists: select best hyps and mutate
# Select parent(s)
parent = 'single' # parent selection method: 'single' or 'weighted'
x = np.loadtxt('evolve.txt', ndmin=2)
n = min(5, len(x)) # number of previous results to consider
x = x[np.argsort(-fitness(x))][:n] # top n mutations
w = fitness(x) - fitness(x).min() # weights
if parent == 'single' or len(x) == 1:
# x = x[random.randint(0, n - 1)] # random selection
x = x[random.choices(range(n), weights=w)[0]] # weighted selection
elif parent == 'weighted':
x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
# Mutate
mp, s = 0.9, 0.2 # mutation probability, sigma
npr = np.random
npr.seed(int(time.time()))
g = np.array([x[0] for x in meta.values()]) # gains 0-1
ng = len(meta)
v = np.ones(ng)
while all(v == 1): # mutate until a change occurs (prevent duplicates)
v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
hyp[k] = float(x[i + 7] * v[i]) # mutate
# Constrain to limits
for k, v in meta.items():
hyp[k] = max(hyp[k], v[1]) # lower limit
hyp[k] = min(hyp[k], v[2]) # upper limit
hyp[k] = round(hyp[k], 5) # significant digits
# Train mutation
results = train(hyp.copy(), opt, device)
# Write mutation results
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))
================================================
FILE: tutorial.ipynb
================================================
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"<a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "HvhYZrIZCEyo"
},
"source": [
"<img src=\"https://user-images.githubusercontent.com/26833433/82952157-51b7db00-9f5d-11ea-8f4b-dda1ffecf992.jpg\">\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": {
"colab_type": "text",
"id": "7mGmQbAO5pQb"
},
"source": [
"# Setup\n",
"\n",
"Clone repo, install dependencies, `%cd` into `./yolov5` folder and check GPU."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
},
"colab_type": "code",
"id": "wbvMlHd_QwMG",
"outputId": "669566b2-391f-4596-f290-110e2e177946"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Setup complete. Using torch 1.5.0 CPU\n"
]
}
],
"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'))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "N3qM6T0W53gh"
},
"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",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 488
},
"colab_type": "code",
"id": "zR9ZbuQCH7FX",
"outputId": "528fcc04-2393-437a-84d2-092becbaefbe"
},
"outputs": [
{
"name": "stdout",
"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"
]
},
{
"data": {
"image/jpeg": 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
gitextract_an16xrke/
├── .dockerignore
├── .gitattributes
├── .gitignore
├── Dockerfile
├── LICENSE
├── README.md
├── detect.py
├── hubconf.py
├── models/
│ ├── __init__.py
│ ├── common.py
│ ├── experimental.py
│ ├── export.py
│ ├── hub/
│ │ ├── yolov3-spp.yaml
│ │ ├── yolov5-fpn.yaml
│ │ └── yolov5-panet.yaml
│ ├── yolo.py
│ ├── yolov5l.yaml
│ ├── yolov5m.yaml
│ ├── yolov5s.yaml
│ └── yolov5x.yaml
├── requirements.txt
├── sotabench.py
├── test.py
├── train.py
├── tutorial.ipynb
├── utils/
│ ├── __init__.py
│ ├── activations.py
│ ├── datasets.py
│ ├── evolve.sh
│ ├── general.py
│ ├── google_utils.py
│ └── torch_utils.py
└── weights/
└── download_weights.sh
SYMBOL INDEX (213 symbols across 13 files)
FILE: detect.py
function detect (line 21) | def detect(save_img=False):
FILE: hubconf.py
function create (line 17) | def create(name, pretrained, channels, classes):
function yolov5s (line 46) | def yolov5s(pretrained=False, channels=3, classes=80):
function yolov5m (line 60) | def yolov5m(pretrained=False, channels=3, classes=80):
function yolov5l (line 74) | def yolov5l(pretrained=False, channels=3, classes=80):
function yolov5x (line 88) | def yolov5x(pretrained=False, channels=3, classes=80):
FILE: models/common.py
function autopad (line 8) | def autopad(k, p=None): # kernel, padding
function DWConv (line 15) | def DWConv(c1, c2, k=1, s=1, act=True):
class Conv (line 20) | class Conv(nn.Module):
method __init__ (line 22) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in,...
method forward (line 28) | def forward(self, x):
method fuseforward (line 31) | def fuseforward(self, x):
class Bottleneck (line 35) | class Bottleneck(nn.Module):
method __init__ (line 37) | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_ou...
method forward (line 44) | def forward(self, x):
class BottleneckCSP (line 48) | class BottleneckCSP(nn.Module):
method __init__ (line 50) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
method forward (line 61) | def forward(self, x):
class SPP (line 67) | class SPP(nn.Module):
method __init__ (line 69) | def __init__(self, c1, c2, k=(5, 9, 13)):
method forward (line 76) | def forward(self, x):
class Focus (line 81) | class Focus(nn.Module):
method __init__ (line 83) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in,...
method forward (line 87) | def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
class Concat (line 91) | class Concat(nn.Module):
method __init__ (line 93) | def __init__(self, dimension=1):
method forward (line 97) | def forward(self, x):
class Flatten (line 101) | class Flatten(nn.Module):
method forward (line 104) | def forward(x):
class Classify (line 108) | class Classify(nn.Module):
method __init__ (line 110) | def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, k...
method forward (line 116) | def forward(self, x):
FILE: models/experimental.py
class CrossConv (line 11) | class CrossConv(nn.Module):
method __init__ (line 13) | def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
method forward (line 21) | def forward(self, x):
class C3 (line 25) | class C3(nn.Module):
method __init__ (line 27) | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ...
method forward (line 38) | def forward(self, x):
class Sum (line 44) | class Sum(nn.Module):
method __init__ (line 46) | def __init__(self, n, weight=False): # n: number of inputs
method forward (line 53) | def forward(self, x):
class GhostConv (line 65) | class GhostConv(nn.Module):
method __init__ (line 67) | def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out,...
method forward (line 73) | def forward(self, x):
class GhostBottleneck (line 78) | class GhostBottleneck(nn.Module):
method __init__ (line 80) | def __init__(self, c1, c2, k, s):
method forward (line 89) | def forward(self, x):
class MixConv2d (line 93) | class MixConv2d(nn.Module):
method __init__ (line 95) | def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
method forward (line 113) | def forward(self, x):
class Ensemble (line 117) | class Ensemble(nn.ModuleList):
method __init__ (line 119) | def __init__(self):
method forward (line 122) | def forward(self, x, augment=False):
function attempt_load (line 132) | def attempt_load(weights, map_location=None):
FILE: models/yolo.py
class Detect (line 19) | class Detect(nn.Module):
method __init__ (line 23) | def __init__(self, nc=80, anchors=(), ch=()): # detection layer
method forward (line 35) | def forward(self, x):
method _make_grid (line 56) | def _make_grid(nx=20, ny=20):
class Model (line 61) | class Model(nn.Module):
method __init__ (line 62) | def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None): # model, input...
method forward (line 95) | def forward(self, x, augment=False, profile=False):
method forward_once (line 115) | def forward_once(self, x, profile=False):
method _initialize_biases (line 140) | def _initialize_biases(self, cf=None): # initialize biases into Detec...
method _print_biases (line 149) | def _print_biases(self):
method fuse (line 160) | def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
method info (line 171) | def info(self, verbose=False): # print model information
function parse_model (line 175) | def parse_model(d, ch): # model_dict, input_channels(3)
FILE: sotabench.py
function test (line 27) | def test(data,
FILE: test.py
function test (line 21) | def test(data,
FILE: train.py
function train (line 36) | def train(hyp, opt, device, tb_writer=None):
FILE: utils/activations.py
class Swish (line 7) | class Swish(nn.Module): #
method forward (line 9) | def forward(x):
class Hardswish (line 13) | class Hardswish(nn.Module): # export-friendly version of nn.Hardswish()
method forward (line 15) | def forward(x):
class MemoryEfficientSwish (line 20) | class MemoryEfficientSwish(nn.Module):
class F (line 21) | class F(torch.autograd.Function):
method forward (line 23) | def forward(ctx, x):
method backward (line 28) | def backward(ctx, grad_output):
method forward (line 33) | def forward(self, x):
class Mish (line 38) | class Mish(nn.Module):
method forward (line 40) | def forward(x):
class MemoryEfficientMish (line 44) | class MemoryEfficientMish(nn.Module):
class F (line 45) | class F(torch.autograd.Function):
method forward (line 47) | def forward(ctx, x):
method backward (line 52) | def backward(ctx, grad_output):
method forward (line 58) | def forward(self, x):
class FReLU (line 63) | class FReLU(nn.Module):
method __init__ (line 64) | def __init__(self, c1, k=3): # ch_in, kernel
method forward (line 69) | def forward(self, x):
FILE: utils/datasets.py
function get_hash (line 29) | def get_hash(files):
function exif_size (line 34) | def exif_size(img):
function create_dataloader (line 49) | def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, au...
class InfiniteDataLoader (line 75) | class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
method __init__ (line 81) | def __init__(self, *args, **kwargs):
method __len__ (line 86) | def __len__(self):
method __iter__ (line 89) | def __iter__(self):
class _RepeatSampler (line 93) | class _RepeatSampler(object):
method __init__ (line 100) | def __init__(self, sampler):
method __iter__ (line 103) | def __iter__(self):
class LoadImages (line 108) | class LoadImages: # for inference
method __init__ (line 109) | def __init__(self, path, img_size=640):
method __iter__ (line 137) | def __iter__(self):
method __next__ (line 141) | def __next__(self):
method new_video (line 180) | def new_video(self, path):
method __len__ (line 185) | def __len__(self):
class LoadWebcam (line 189) | class LoadWebcam: # for inference
method __init__ (line 190) | def __init__(self, pipe=0, img_size=640):
method __iter__ (line 211) | def __iter__(self):
method __next__ (line 215) | def __next__(self):
method __len__ (line 250) | def __len__(self):
class LoadStreams (line 254) | class LoadStreams: # multiple IP or RTSP cameras
method __init__ (line 255) | def __init__(self, sources='streams.txt', img_size=640):
method update (line 288) | def update(self, index, cap):
method __iter__ (line 300) | def __iter__(self):
method __next__ (line 304) | def __next__(self):
method __len__ (line 323) | def __len__(self):
class LoadImagesAndLabels (line 327) | class LoadImagesAndLabels(Dataset): # for training/testing
method __init__ (line 328) | def __init__(self, path, img_size=640, batch_size=16, augment=False, h...
method cache_labels (line 478) | def cache_labels(self, path='labels.cache'):
method __len__ (line 504) | def __len__(self):
method __getitem__ (line 513) | def __getitem__(self, index):
method collate_fn (line 597) | def collate_fn(batch):
function load_image (line 605) | def load_image(self, index):
function augment_hsv (line 622) | def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
function load_mosaic (line 641) | def load_mosaic(self, index):
function replicate (line 699) | def replicate(img, labels):
function letterbox (line 716) | def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=Tru...
function random_perspective (line 749) | def random_perspective(img, targets=(), degrees=10, translate=.1, scale=...
function box_candidates (line 836) | def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1): # bo...
function cutout (line 844) | def cutout(image, labels):
function reduce_img_size (line 890) | def reduce_img_size(path='path/images', img_size=1024): # from utils.da...
function recursive_dataset2bmp (line 907) | def recursive_dataset2bmp(dataset='path/dataset_bmp'): # from utils.dat...
function imagelist2folder (line 927) | def imagelist2folder(path='path/images.txt'): # from utils.datasets imp...
function create_folder (line 936) | def create_folder(path='./new'):
FILE: utils/general.py
function torch_distributed_zero_first (line 39) | def torch_distributed_zero_first(local_rank: int):
function set_logging (line 50) | def set_logging(rank=-1):
function init_seeds (line 56) | def init_seeds(seed=0):
function get_latest_run (line 62) | def get_latest_run(search_dir='./runs'):
function check_git_status (line 68) | def check_git_status():
function check_img_size (line 76) | def check_img_size(img_size, s=32):
function check_anchors (line 84) | def check_anchors(dataset, model, thr=4.0, imgsz=640):
function check_anchor_order (line 118) | def check_anchor_order(m):
function check_file (line 129) | def check_file(file):
function check_dataset (line 139) | def check_dataset(dict):
function make_divisible (line 159) | def make_divisible(x, divisor):
function labels_to_class_weights (line 164) | def labels_to_class_weights(labels, nc=80):
function labels_to_image_weights (line 183) | def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
function coco80_to_coco91_class (line 192) | def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index...
function xyxy2xywh (line 204) | def xyxy2xywh(x):
function xywh2xyxy (line 214) | def xywh2xyxy(x):
function scale_coords (line 224) | def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
function clip_coords (line 240) | def clip_coords(boxes, img_shape):
function ap_per_class (line 248) | def ap_per_class(tp, conf, pred_cls, target_cls):
function compute_ap (line 311) | def compute_ap(recall, precision):
function bbox_iou (line 340) | def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=Fal...
function box_iou (line 385) | def box_iou(box1, box2):
function wh_iou (line 410) | def wh_iou(wh1, wh2):
class FocalLoss (line 418) | class FocalLoss(nn.Module):
method __init__ (line 420) | def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
method forward (line 428) | def forward(self, pred, true):
function smooth_BCE (line 448) | def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues...
class BCEBlurWithLogitsLoss (line 453) | class BCEBlurWithLogitsLoss(nn.Module):
method __init__ (line 455) | def __init__(self, alpha=0.05):
method forward (line 460) | def forward(self, pred, true):
function compute_loss (line 470) | def compute_loss(p, targets, model): # predictions, targets, model
function build_targets (line 533) | def build_targets(p, targets, model):
function non_max_suppression (line 590) | def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, merge...
function strip_optimizer (line 672) | def strip_optimizer(f='weights/best.pt', s=''): # from utils.general im...
function coco_class_count (line 686) | def coco_class_count(path='../coco/labels/train2014/'):
function coco_only_people (line 697) | def coco_only_people(path='../coco/labels/train2017/'): # from utils.ge...
function crop_images_random (line 706) | def crop_images_random(path='../images/', scale=0.50): # from utils.gen...
function coco_single_class_labels (line 729) | def coco_single_class_labels(path='../coco/labels/train2014/', label_cla...
function kmean_anchors (line 751) | def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0...
function print_mutation (line 850) | def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''):
function apply_classifier (line 881) | def apply_classifier(x, model, img, im0):
function fitness (line 916) | def fitness(x):
function output_to_target (line 922) | def output_to_target(output, width, height):
function increment_dir (line 944) | def increment_dir(dir, comment=''):
function hist2d (line 955) | def hist2d(x, y, n=100):
function butter_lowpass_filtfilt (line 964) | def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
function plot_one_box (line 976) | def plot_one_box(x, img, color=None, label=None, line_thickness=None):
function plot_wh_methods (line 990) | def plot_wh_methods(): # from utils.general import *; plot_wh_methods()
function plot_images (line 1011) | def plot_images(images, targets, paths=None, fname='images.jpg', names=N...
function plot_lr_scheduler (line 1094) | def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
function plot_test_txt (line 1111) | def plot_test_txt(): # from utils.general import *; plot_test()
function plot_targets_txt (line 1128) | def plot_targets_txt(): # from utils.general import *; plot_targets_txt()
function plot_study_txt (line 1141) | def plot_study_txt(f='study.txt', x=None): # from utils.general import ...
function plot_labels (line 1173) | def plot_labels(labels, save_dir=''):
function plot_evolution (line 1205) | def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.ge...
function plot_results_overlay (line 1229) | def plot_results_overlay(start=0, stop=0): # from utils.general import ...
function plot_results (line 1252) | def plot_results(start=0, stop=0, bucket='', id=(), labels=(),
FILE: utils/google_utils.py
function gsutil_getsize (line 14) | def gsutil_getsize(url=''):
function attempt_download (line 20) | def attempt_download(weights):
function gdrive_download (line 56) | def gdrive_download(id='1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', name='coco12...
function get_token (line 90) | def get_token(cookie="./cookie"):
FILE: utils/torch_utils.py
function init_seeds (line 16) | def init_seeds(seed=0):
function select_device (line 28) | def select_device(device='', batch_size=None):
function time_synchronized (line 55) | def time_synchronized():
function is_parallel (line 60) | def is_parallel(model):
function intersect_dicts (line 64) | def intersect_dicts(da, db, exclude=()):
function initialize_weights (line 69) | def initialize_weights(model):
function find_modules (line 81) | def find_modules(model, mclass=nn.Conv2d):
function sparsity (line 86) | def sparsity(model):
function prune (line 95) | def prune(model, amount=0.3):
function fuse_conv_and_bn (line 106) | def fuse_conv_and_bn(conv, bn):
function model_info (line 131) | def model_info(model, verbose=False):
function load_classifier (line 153) | def load_classifier(name='resnet101', n=2):
function scale_img (line 174) | def scale_img(img, ratio=1.0, same_shape=False): # img(16,3,256,416), r...
function copy_attr (line 188) | def copy_attr(a, b, include=(), exclude=()):
class ModelEMA (line 197) | class ModelEMA:
method __init__ (line 207) | def __init__(self, model, decay=0.9999, updates=0):
method update (line 217) | def update(self, model):
method update_attr (line 229) | def update_attr(self, model, include=(), exclude=('process_group', 're...
Condensed preview — 33 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (3,558K chars).
[
{
"path": ".dockerignore",
"chars": 3602,
"preview": "# Repo-specific DockerIgnore -------------------------------------------------------------------------------------------"
},
{
"path": ".gitattributes",
"chars": 75,
"preview": "# this drop notebooks from GitHub language stats\n*.ipynb linguist-vendored\n"
},
{
"path": ".gitignore",
"chars": 3825,
"preview": "# Repo-specific GitIgnore ----------------------------------------------------------------------------------------------"
},
{
"path": "Dockerfile",
"chars": 1804,
"preview": "# Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch\nFROM nvcr.io/nvidia/pytorch:2"
},
{
"path": "LICENSE",
"chars": 35126,
"preview": "GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free Software Foundation,"
},
{
"path": "README.md",
"chars": 12400,
"preview": "# 基于 YOLOv5 的卫星图像目标检测 \n\n\n\n## 1 数据准备\n\n\n\n### 1.1 数据集采集\n\n本项目使用 DOTA 数据集，数据集链接：[下载地址](https://captain-whu.github.io/DOTA/dat"
},
{
"path": "detect.py",
"chars": 7642,
"preview": "import argparse\nimport os\nimport platform\nimport shutil\nimport time\nfrom pathlib import Path\n\nimport cv2\nimport torch\nim"
},
{
"path": "hubconf.py",
"chars": 3358,
"preview": "\"\"\"File for accessing YOLOv5 via PyTorch Hub https://pytorch.org/hub/\n\nUsage:\n import torch\n model = torch.hub.loa"
},
{
"path": "models/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "models/common.py",
"chars": 4281,
"preview": "# This file contains modules common to various models\nimport math\n\nimport torch\nimport torch.nn as nn\n\n\ndef autopad(k, p"
},
{
"path": "models/experimental.py",
"chars": 5553,
"preview": "# This file contains experimental modules\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom models.common imp"
},
{
"path": "models/export.py",
"chars": 3374,
"preview": "\"\"\"Exports a YOLOv5 *.pt model to ONNX and TorchScript formats\n\nUsage:\n $ export PYTHONPATH=\"$PWD\" && python models/e"
},
{
"path": "models/hub/yolov3-spp.yaml",
"chars": 1530,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "models/hub/yolov5-fpn.yaml",
"chars": 1244,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "models/hub/yolov5-panet.yaml",
"chars": 1458,
"preview": "# parameters\nnc: 80 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "models/yolo.py",
"chars": 11535,
"preview": "import argparse\nimport logging\nimport math\nfrom copy import deepcopy\nfrom pathlib import Path\n\nimport torch\nimport torch"
},
{
"path": "models/yolov5l.yaml",
"chars": 1453,
"preview": "# parameters\nnc: 16 # number of classes\ndepth_multiple: 1.0 # model depth multiple\nwidth_multiple: 1.0 # layer channe"
},
{
"path": "models/yolov5m.yaml",
"chars": 1455,
"preview": "# parameters\nnc: 16 # number of classes\ndepth_multiple: 0.67 # model depth multiple\nwidth_multiple: 0.75 # layer chan"
},
{
"path": "models/yolov5s.yaml",
"chars": 1455,
"preview": "# parameters\nnc: 16 # number of classes\ndepth_multiple: 0.33 # model depth multiple\nwidth_multiple: 0.50 # layer chan"
},
{
"path": "models/yolov5x.yaml",
"chars": 1455,
"preview": "# parameters\nnc: 16 # number of classes\ndepth_multiple: 1.33 # model depth multiple\nwidth_multiple: 1.25 # layer chan"
},
{
"path": "requirements.txt",
"chars": 569,
"preview": "# pip install -r requirements.txt\n\n# base ----------------------------------------\nCython\nmatplotlib>=3.2.2\nnumpy>=1.18."
},
{
"path": "sotabench.py",
"chars": 14354,
"preview": "import argparse\nimport glob\nimport json\nimport os\nimport shutil\nfrom pathlib import Path\n\nimport numpy as np\nimport torc"
},
{
"path": "test.py",
"chars": 13620,
"preview": "import argparse\nimport glob\nimport json\nimport os\nimport shutil\nfrom pathlib import Path\n\nimport numpy as np\nimport torc"
},
{
"path": "train.py",
"chars": 27722,
"preview": "import argparse\nimport glob\nimport logging\nimport math\nimport os\nimport random\nimport shutil\nimport time\nfrom pathlib im"
},
{
"path": "tutorial.ipynb",
"chars": 3273734,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"view-in-github\"\n }"
},
{
"path": "utils/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "utils/activations.py",
"chars": 2176,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\n# Swish https://arxiv.org/pdf/1905.02244.pdf ------"
},
{
"path": "utils/datasets.py",
"chars": 38988,
"preview": "import glob\nimport math\nimport os\nimport random\nimport shutil\nimport time\nfrom pathlib import Path\nfrom threading import"
},
{
"path": "utils/evolve.sh",
"chars": 747,
"preview": "#!/bin/bash\n# Hyperparameter evolution commands (avoids CUDA memory leakage issues)\n# Replaces train.py python generatio"
},
{
"path": "utils/general.py",
"chars": 53641,
"preview": "import glob\nimport logging\nimport math\nimport os\nimport platform\nimport random\nimport shutil\nimport subprocess\nimport ti"
},
{
"path": "utils/google_utils.py",
"chars": 4971,
"preview": "# This file contains google utils: https://cloud.google.com/storage/docs/reference/libraries\n# pip install --upgrade goo"
},
{
"path": "utils/torch_utils.py",
"chars": 8991,
"preview": "import logging\nimport math\nimport os\nimport time\nfrom copy import deepcopy\n\nimport torch\nimport torch.backends.cudnn as "
},
{
"path": "weights/download_weights.sh",
"chars": 245,
"preview": "#!/bin/bash\n# Download common models\n\npython -c \"\nfrom utils.google_utils import *;\nattempt_download('weights/yolov5s.pt"
}
]
About this extraction
This page contains the full source code of the KevinMuyaoGuo/yolov5s_for_satellite_imagery GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 33 files (3.4 MB), approximately 887.0k tokens, and a symbol index with 213 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.