diff --git a/README.md b/README.md index 0f38eb8f7d52a8d5c46a8e3043f0a18dd51bc4df..b659eb941ed3cf0f87bb77263f6374c77ffd0f59 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,10 @@ --- -title: Lung Nodule-Segmentation -emoji: 📉 -colorFrom: green -colorTo: indigo +title: Lung Nodules Segmentation +emoji: ⚡ +colorFrom: indigo +colorTo: purple sdk: gradio -sdk_version: 4.38.1 +sdk_version: 4.37.2 app_file: app.py pinned: false license: mit diff --git a/YOLOv5-Streamlit-Deployment/.gitignore b/YOLOv5-Streamlit-Deployment/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..a5f95ae5d9b9fc43eb31cfe3458ae229acbf05f7 --- /dev/null +++ b/YOLOv5-Streamlit-Deployment/.gitignore @@ -0,0 +1,255 @@ +# Repo-specific GitIgnore ---------------------------------------------------------------------------------------------- +*.jpg +*.jpeg +*.png +*.bmp +*.tif +*.tiff +*.heic +*.JPG +*.JPEG +*.PNG +*.BMP +*.TIF +*.TIFF +*.HEIC +*.mp4 +*.mov +*.MOV +*.avi +*.data +*.json +*.cfg +!setup.cfg +!cfg/yolov3*.cfg + +storage.googleapis.com +runs/* + + +!data/*.yaml +!data/hyps +!data/scripts + +!data/*.sh + +results*.csv + +# Datasets ------------------------------------------------------------------------------------------------------------- +coco/ +coco128/ +VOC/ + +# MATLAB GitIgnore ----------------------------------------------------------------------------------------------------- +*.m~ +*.mat +!targets*.mat + +# Neural Network weights ----------------------------------------------------------------------------------------------- +*.weights +*.pt +*.pb +*.onnx +*.engine +*.mlmodel +*.torchscript +*.tflite +*.h5 +*_saved_model/ +*_web_model/ +*_openvino_model/ +darknet53.conv.74 +yolov3-tiny.conv.15 + +# GitHub Python GitIgnore ---------------------------------------------------------------------------------------------- +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +/wandb/ +.installed.cfg +*.egg + + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv* +venv*/ +ENV*/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + + +# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore ----------------------------------------------- + +# General +*/.DS_Store +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon +Icon? + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + + +# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff: +.idea/* +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/dictionaries +.html # Bokeh Plots +.pg # TensorFlow Frozen Graphs +.avi # videos + +# Sensitive or high-churn files: +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml + +# Gradle: +.idea/**/gradle.xml +.idea/**/libraries + +# CMake +cmake-build-debug/ +cmake-build-release/ + +# Mongo Explorer plugin: +.idea/**/mongoSettings.xml + +## File-based project format: +*.iws + +## Plugin-specific files: + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties diff --git a/YOLOv5-Streamlit-Deployment/.pre-commit-config.yaml b/YOLOv5-Streamlit-Deployment/.pre-commit-config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8505274918599e83376eb930a522b6472f29e1ee --- /dev/null +++ b/YOLOv5-Streamlit-Deployment/.pre-commit-config.yaml @@ -0,0 +1,67 @@ +# Define hooks for code formations +# Will be applied on any updated commit files if a user has installed and linked commit hook + +default_language_version: + python: python3.8 + +# Define bot property if installed via https://github.com/marketplace/pre-commit-ci +ci: + autofix_prs: true + autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions' + autoupdate_schedule: monthly + # submodules: true + +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.2.0 + hooks: + - id: end-of-file-fixer + - id: trailing-whitespace + - id: check-case-conflict + - id: check-yaml + - id: check-toml + - id: pretty-format-json + - id: check-docstring-first + + - repo: https://github.com/asottile/pyupgrade + rev: v2.32.1 + hooks: + - id: pyupgrade + name: Upgrade code + args: [ --py37-plus ] + + - repo: https://github.com/PyCQA/isort + rev: 5.10.1 + hooks: + - id: isort + name: Sort imports + + - repo: https://github.com/pre-commit/mirrors-yapf + rev: v0.32.0 + hooks: + - id: yapf + name: YAPF formatting + + - repo: https://github.com/executablebooks/mdformat + rev: 0.7.14 + hooks: + - id: mdformat + name: MD formatting + additional_dependencies: + - mdformat-gfm + - mdformat-black + exclude: | + (?x)^( + README.md + )$ + + - repo: https://github.com/asottile/yesqa + rev: v1.3.0 + hooks: + - id: yesqa + + - repo: https://github.com/PyCQA/flake8 + rev: 4.0.1 + hooks: + - id: flake8 + name: PEP8 diff --git a/YOLOv5-Streamlit-Deployment/LICENSE b/YOLOv5-Streamlit-Deployment/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..212d164a4871d3e81322b906040d24e3ac1f27e9 --- /dev/null +++ b/YOLOv5-Streamlit-Deployment/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Thepbordin Jaiinsom + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/YOLOv5-Streamlit-Deployment/README.md b/YOLOv5-Streamlit-Deployment/README.md new file mode 100644 index 0000000000000000000000000000000000000000..34e314498c3821cd1ce66939a0eb1e7a4d52ddf2 --- /dev/null +++ b/YOLOv5-Streamlit-Deployment/README.md @@ -0,0 +1,74 @@ + +# 🚀 YOLOv5 Streamlit Deployment +[![HitCount](https://hits.dwyl.com/thepbordin/YOLOv5-Streamlit-Deployment.svg?style=flat&show=unique)](http://hits.dwyl.com/thepbordin/YOLOv5-Streamlit-Deployment) + + +A Easy way to deploy [YOLOv5](https://github.com/ultralytics/yolov5) object detection model with [Streamlit](https://streamlit.io/). + +**Please feel free to use/edit.** + + +code modified by GitHub/thepbordin from GitHub/zhoroh + +## ✨ Features + +- YOLO Weights Source + - Load from Local + - Download Weights from URL +- Example Dataset + - Videos + - Images + - Upload Data + - Video + - Image +- Select computing device (cuda/cpu) + + + +## ⚙️ Installation + + + +### Local Use +1. Install Requirements + `pip install -r requirements.txt` +2. Install ffmpeg (for video inferencing) + - For Windows [read here](https://www.geeksforgeeks.org/how-to-install-ffmpeg-on-windows/) + - For Mac (brew) + `brew install ffmpeg` +3. Strart Stremlit + ``` + cd YOLOv5-Streamlit-Deployment + streamlit run app.py + ``` +### Streamlit Cloud +1. Edit a configuration in app.py (read ⚙️ Config Instruction) +2. (Optional) Upload example datas in + - `example_images` + - `example_videos` +4. Deploy on [Streamlit](https://share.streamlit.io/deploy) + + +## ⚙️ Config Instruction +### Download model from URL + +1. Upload model to [Internet Archive](https://archive.org/) +2. Go to your uploaded file page. +3. From `DOWNLOAD OPTIONS` select `SHOW ALL` +4. Right click at .pt and Copy link address. +5. Edit config in [app.py](https://github.com/thepbordin/YOLOv5-Streamlit-Deployment/blob/main/app.py) + + ```python + cfg_enable_url_download = True + url = "your_model_url" + ``` + +### Use local .pt file: +Edit config in [app.py](https://github.com/thepbordin/YOLOv5-Streamlit-Deployment/blob/main/app.py) +```python +## CFG +cfg_model_path = "models/your_model_name.pt" +``` + +## Reference +[Yolov5 Real-time Inference using Streamlit](https://github.com/moaaztaha/Yolo-Interface-using-Streamlit) diff --git a/YOLOv5-Streamlit-Deployment/app.py b/YOLOv5-Streamlit-Deployment/app.py new file mode 100644 index 0000000000000000000000000000000000000000..64b778a724a56cfdae317134e04f388e9a415739 --- /dev/null +++ b/YOLOv5-Streamlit-Deployment/app.py @@ -0,0 +1,182 @@ +import streamlit as st +import torch +from PIL import Image +from io import * +import glob +from datetime import datetime +import os +import wget +from video_predict import runVideo + + +# Configurations +CFG_MODEL_PATH = "models/yourModel.pt" +CFG_ENABLE_URL_DOWNLOAD = True +CFG_ENABLE_VIDEO_PREDICTION = True +if CFG_ENABLE_URL_DOWNLOAD: + # Configure this if you set cfg_enable_url_download to True + url = "https://archive.org/download/yoloTrained/yoloTrained.pt" +# End of Configurations + + +def imageInput(model, src): + + if src == 'Upload your own data.': + image_file = st.file_uploader( + "Upload An Image", type=['png', 'jpeg', 'jpg']) + col1, col2 = st.columns(2) + if image_file is not None: + img = Image.open(image_file) + with col1: + st.image(img, caption='Uploaded Image', + use_column_width='always') + ts = datetime.timestamp(datetime.now()) + imgpath = os.path.join('data/uploads', str(ts)+image_file.name) + outputpath = os.path.join( + 'data/outputs', os.path.basename(imgpath)) + with open(imgpath, mode="wb") as f: + f.write(image_file.getbuffer()) + + with st.spinner(text="Predicting..."): + # Load model + pred = model(imgpath) + pred.render() + # save output to file + for im in pred.ims: + im_base64 = Image.fromarray(im) + im_base64.save(outputpath) + + # Predictions + img_ = Image.open(outputpath) + with col2: + st.image(img_, caption='Model Prediction(s)', + use_column_width='always') + + elif src == 'From example data.': + # Image selector slider + imgpaths = glob.glob('data/example_images/*') + if len(imgpaths) == 0: + st.write(".") + st.error( + 'No images found, Please upload example images in data/example_images', icon="") + return + imgsel = st.slider('Select random images from example data.', + min_value=1, max_value=len(imgpaths), step=1) + image_file = imgpaths[imgsel-1] + submit = st.button("Predict!") + col1, col2 = st.columns(2) + with col1: + img = Image.open(image_file) + st.image(img, caption='Selected Image', use_column_width='always') + with col2: + if image_file is not None and submit: + with st.spinner(text="Predicting..."): + # Load model + + pred = model(image_file) + pred.render() + # save output to file + for im in pred.ims: + im_base64 = Image.fromarray(im) + im_base64.save(os.path.join( + 'data/outputs', os.path.basename(image_file))) + # Display predicton + img_ = Image.open(os.path.join( + 'data/outputs', os.path.basename(image_file))) + st.image(img_, caption='Model Prediction(s)') + + +def videoInput(model, src): + if src == 'Upload your own data.': + uploaded_video = st.file_uploader( + "Upload A Video", type=['mp4', 'mpeg', 'mov']) + pred_view = st.empty() + warning = st.empty() + if uploaded_video != None: + + # Save video to disk + ts = datetime.timestamp(datetime.now()) # timestamp a upload + uploaded_video_path = os.path.join( + 'data/uploads', str(ts)+uploaded_video.name) + with open(uploaded_video_path, mode='wb') as f: + f.write(uploaded_video.read()) + + # Display uploaded video + with open(uploaded_video_path, 'rb') as f: + video_bytes = f.read() + st.video(video_bytes) + st.write("Uploaded Video") + submit = st.button("Run Prediction") + if submit: + runVideo(model, uploaded_video_path, pred_view, warning) + + elif src == 'From example data.': + # Image selector slider + videopaths = glob.glob('data/example_videos/*') + if len(videopaths) == 0: + st.error( + 'No videos found, Please upload example videos in data/example_videos', icon="⚠️") + return + imgsel = st.slider('Select random video from example data.', + min_value=1, max_value=len(videopaths), step=1) + pred_view = st.empty() + video = videopaths[imgsel-1] + submit = st.button("Predict!") + if submit: + runVideo(model, video, pred_view, warning) + + +def main(): + if CFG_ENABLE_URL_DOWNLOAD: + downloadModel() + + else: + if not os.path.exists(CFG_MODEL_PATH): + st.error( + 'Model not found, please config if you wish to download model from url set `cfg_enable_url_download = True` ', icon="⚠️") + + # -- Sidebar + st.sidebar.title('⚙️ Options') + datasrc = st.sidebar.radio("Select input source.", [ + 'From example data.', 'Upload your own data.']) + + if CFG_ENABLE_VIDEO_PREDICTION: + option = st.sidebar.radio("Select input type.", ['Image', 'Video']) + else: + option = st.sidebar.radio("Select input type.", ['Image']) + if torch.cuda.is_available(): + deviceoption = st.sidebar.radio("Select compute Device.", [ + 'cpu', 'cuda'], disabled=False, index=1) + else: + deviceoption = st.sidebar.radio("Select compute Device.", [ + 'cpu', 'cuda'], disabled=True, index=0) + # -- End of Sidebar + + st.header('📦 YOLOv5 Streamlit Deployment Example') + st.sidebar.markdown( + "https://github.com/thepbordin/Obstacle-Detection-for-Blind-people-Deployment") + + if option == "Image": + imageInput(loadmodel(deviceoption), datasrc) + elif option == "Video": + videoInput(loadmodel(deviceoption), datasrc) + + +# Downlaod Model from url. +@st.cache_resource +def downloadModel(): + if not os.path.exists(CFG_MODEL_PATH): + wget.download(url, out="models/") + + +@st.cache_resource +def loadmodel(device): + if CFG_ENABLE_URL_DOWNLOAD: + CFG_MODEL_PATH = f"models/{url.split('/')[-1:][0]}" + model = torch.hub.load('ultralytics/yolov5', 'custom', + path=CFG_MODEL_PATH, force_reload=True, device=device) + return model + + +if __name__ == '__main__': + main() diff --git a/YOLOv5-Streamlit-Deployment/data/.DS_Store b/YOLOv5-Streamlit-Deployment/data/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..9ea2d7a140d3fb7ee4a918a6258784357eadea0a Binary files /dev/null and b/YOLOv5-Streamlit-Deployment/data/.DS_Store differ diff --git a/YOLOv5-Streamlit-Deployment/data/example_images/.DS_Store b/YOLOv5-Streamlit-Deployment/data/example_images/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..2f4e94a0a1f488eb1a30c360aecd0310fa030821 Binary files /dev/null and b/YOLOv5-Streamlit-Deployment/data/example_images/.DS_Store differ diff --git a/YOLOv5-Streamlit-Deployment/data/example_images/.gitignore b/YOLOv5-Streamlit-Deployment/data/example_images/.gitignore new file mode 100644 index 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Except this file +!.gitignore \ No newline at end of file diff --git a/YOLOv5-Streamlit-Deployment/data/uploads/.DS_Store b/YOLOv5-Streamlit-Deployment/data/uploads/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..8687ff5a254a6a254c9a428b6f85328b50d46048 Binary files /dev/null and b/YOLOv5-Streamlit-Deployment/data/uploads/.DS_Store differ diff --git a/YOLOv5-Streamlit-Deployment/data/uploads/.gitignore b/YOLOv5-Streamlit-Deployment/data/uploads/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..86d0cb2726c6c7c179b99520c452dd1b68e7a813 --- /dev/null +++ b/YOLOv5-Streamlit-Deployment/data/uploads/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/YOLOv5-Streamlit-Deployment/data/video_frames/.DS_Store b/YOLOv5-Streamlit-Deployment/data/video_frames/.DS_Store new file mode 100644 index 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a/YOLOv5-Streamlit-Deployment/data/video_output/.gitignore b/YOLOv5-Streamlit-Deployment/data/video_output/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..86d0cb2726c6c7c179b99520c452dd1b68e7a813 --- /dev/null +++ b/YOLOv5-Streamlit-Deployment/data/video_output/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/YOLOv5-Streamlit-Deployment/models/.gitignore b/YOLOv5-Streamlit-Deployment/models/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..86d0cb2726c6c7c179b99520c452dd1b68e7a813 --- /dev/null +++ b/YOLOv5-Streamlit-Deployment/models/.gitignore @@ -0,0 +1,4 @@ +# Ignore everything in this directory +* +# Except this file +!.gitignore \ No newline at end of file diff --git a/YOLOv5-Streamlit-Deployment/packages.txt b/YOLOv5-Streamlit-Deployment/packages.txt new file mode 100644 index 0000000000000000000000000000000000000000..c5b9bc1f242a14c5fbab758590839acbc8bc6d10 --- /dev/null +++ b/YOLOv5-Streamlit-Deployment/packages.txt @@ -0,0 +1,2 @@ +libgl1 +ffmpeg \ No newline at end of file diff --git a/YOLOv5-Streamlit-Deployment/requirements.txt b/YOLOv5-Streamlit-Deployment/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..da246a1127d683d8280607508240eb54491d2f26 --- /dev/null +++ b/YOLOv5-Streamlit-Deployment/requirements.txt @@ -0,0 +1,32 @@ +# YOLOv5 requirements +# Usage: pip install -r requirements.txt + +# Base ---------------------------------------- +matplotlib>=3.2.2 +numpy>=1.18.5 +opencv-python +Pillow>=7.1.2 +PyYAML>=5.3.1 +requests>=2.23.0 +scipy>=1.4.1 # Google Colab version +torch>=1.7.0 +torchvision>=0.8.1 +tqdm>=4.41.0 +protobuf<4.21.5 # https://github.com/ultralytics/yolov5/issues/8012 +ultralytics +# Logging ------------------------------------- +tensorboard>=2.4.1 +# wandb + +# Plotting ------------------------------------ +pandas>=1.1.4 +seaborn>=0.11.0 + + +# Extras -------------------------------------- +ipython # interactive notebook +psutil # system utilization +thop # FLOPs computation +streamlit +wget +ffmpeg-python diff --git a/YOLOv5-Streamlit-Deployment/video_predict.py b/YOLOv5-Streamlit-Deployment/video_predict.py new file mode 100644 index 0000000000000000000000000000000000000000..97e7a79f99578a8bb6ceb7d9de94c651319029fa --- /dev/null +++ b/YOLOv5-Streamlit-Deployment/video_predict.py @@ -0,0 +1,45 @@ +import os +import os.path as osp +import cv2 +from PIL import Image +import streamlit as st + + +def runVideo(model, video, vdo_view, warn): + video_name = osp.basename(video) + outputpath = osp.join('data/video_output', video_name) + + # Create A Dir to save Video Frames + os.makedirs('data/video_frames', exist_ok=True) + frames_dir = osp.join('data/video_frames', video_name) + os.makedirs(frames_dir, exist_ok=True) + cap = cv2.VideoCapture(video) + frame_count = 0 + with st.spinner(text="Predicting..."): + warn.warning( + 'This is realtime prediction, If you wish to download the final prediction result wait until the process done.', icon="⚠️") + while True: + frame_count += 1 + ret, frame = cap.read() + if ret == False: + break + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + result = model(frame) + result.render() + image = Image.fromarray(result.ims[0]) + vdo_view.image(image, caption='Current Model Prediction(s)') + image.save(osp.join(frames_dir, f'{frame_count}.jpg')) + cap.release() + # convert frames in dir to a single video file + os.system( + f'ffmpeg -framerate 30 -i {frames_dir}/%d.jpg -c:v libx264 -pix_fmt yuv420p {outputpath}') + # Clean up Frames Dir + os.system(f'rm -rf {frames_dir}') + + # Display Video + output_video = open(outputpath, 'rb') + output_video_bytes = output_video.read() + st.video(output_video_bytes) + st.write("Model Prediction") + vdo_view.empty() + warn.empty() diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..752a0781086a78ff02622151041ebde49240ebdc --- /dev/null +++ b/app.py @@ -0,0 +1,121 @@ +import gradio as gr +import cv2 +import requests +import os +import torch +import numpy as np +from yolov5.models.experimental import attempt_load +from yolov5.utils.general import non_max_suppression +from yolov5.utils.augmentations import letterbox + +# Example URLs for downloading images +file_urls = [ + "https://www.dropbox.com/scl/fi/n3bs5xnl2kanqmwv483k3/1_jpg.rf.4a59a63d0a7339d280dd18ef3c2e675a.jpg?rlkey=4n9dnls1byb4wm54ycxzx3ovi&st=ue5xv8yx&dl=0", + "https://www.dropbox.com/scl/fi/asrmao4b4fpsrhqex8kog/2_jpg.rf.b87583d95aa220d4b7b532ae1948e7b7.jpg?rlkey=jkmux5jjy8euzhxizupdmpesb&st=v3ld14tx&dl=0", + "https://www.dropbox.com/scl/fi/fi0e8zxqqy06asnu0robz/3_jpg.rf.d2932cce7e88c2675e300ececf9f1b82.jpg?rlkey=hfdqwxkxetabe38ukzbb39pl5&st=ga1uouhj&dl=0", + "https://www.dropbox.com/scl/fi/ruobyat1ld1c33ch5yjpv/4_jpg.rf.3395c50b4db0ec0ed3448276965b2459.jpg?rlkey=j1m4qa0pmdh3rlr344v82u3am&st=lex8h3qi&dl=0", + "https://www.dropbox.com/scl/fi/ok3izk4jj1pg6psxja3aj/5_jpg.rf.62f3dc64b6c894fbb165d8f6e2ee1382.jpg?rlkey=euu16z8fd8u8za4aflvu5qg4v&st=pwno39nc&dl=0", + "https://www.dropbox.com/scl/fi/8r1fpwxkwq7c2i6ky6qv5/10_jpg.rf.c1785c33dd3552e860bf043c2fd0a379.jpg?rlkey=fcw41ppgzu0ao7xo6ijbpdi4c&st=to2udvxb&dl=0", + "https://www.dropbox.com/scl/fi/ihiid7hbz1vvaoqrstwa5/7_jpg.rf.dfc30f9dc198cf6697d9023ac076e822.jpg?rlkey=yh67p4ex52wn9t0bfw0jr77ef&st=02qw80xa&dl=0", +] + +def download_file(url, save_name): + """Downloads a file from a URL.""" + if not os.path.exists(save_name): + file = requests.get(url) + with open(save_name, 'wb') as f: + f.write(file.content) + +# Download images +for i, url in enumerate(file_urls): + download_file(url, f"image_{i}.jpg") + +# Load YOLOv5 model (placeholder) +model_path = "/Users/basilshaji/Desktop/Lung_Nodules_Segmentation/best.pt" # Path to your YOLOv5 model +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Use GPU if available +model = attempt_load(model_path, device=device) # Placeholder for model loading +model.eval() # Set the model to evaluation mode + +def preprocess_image(image_path): + img0 = cv2.imread(image_path) + img = letterbox(img0, 640, stride=32, auto=True)[0] # Resize and pad to 640x640 + img = img.transpose(2, 0, 1)[::-1] # Convert BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + img = torch.from_numpy(img).to(device) + img = img.float() # uint8 to fp16/32 + img /= 255.0 # 0 - 255 to 0.0 - 1.0 + if img.ndimension() == 3: + img = img.unsqueeze(0) + return img, img0 + +def infer(model, img): + with torch.no_grad(): + pred = model(img)[0] + return pred + +def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0] + pad = ratio_pad[1] + + coords[:, [0, 2]] -= pad[0] # x padding + coords[:, [1, 3]] -= pad[1] # y padding + coords[:, :4] /= gain + coords[:, :4].clip_(min=0, max=img1_shape[0]) # clip boxes + return coords + +def postprocess(pred, img0_shape, img): + pred = non_max_suppression(pred, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False) + results = [] + for det in pred: # detections per image + if len(det): + det[:, :4] = scale_coords(img.shape[2:], det[:, :4], img0_shape).round() + for *xyxy, conf, cls in reversed(det): + results.append((xyxy, conf, cls)) + return results + +def detect_objects(image_path): + img, img0 = preprocess_image(image_path) + pred = infer(model, img) + results = postprocess(pred, img0.shape, img) + return results + +def draw_bounding_boxes(img, results): + for (x1, y1, x2, y2), conf, cls in results: + x1, y1, x2, y2 = map(int, [x1, y1, x2, y2]) + cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2) + cv2.putText(img, f'{model.names[int(cls)]} {conf:.2f}', (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (36, 255, 12), 2) + return img + +def show_preds_image(filepath): + results = detect_objects(filepath) + img0 = cv2.imread(filepath) + img_with_boxes = draw_bounding_boxes(img0, results) + return cv2.cvtColor(img_with_boxes, cv2.COLOR_BGR2RGB) + +# Define Gradio components +input_component = gr.components.Image(type="filepath", label="Input Image") +output_component = gr.components.Image(type="numpy", label="Output Image") + +# Create Gradio interface +interface = gr.Interface( + fn=show_preds_image, + inputs=input_component, + outputs=output_component, + title="Lung Nodule Detection [ Segmentation Model ]", + examples=[ + "/Users/basilshaji/Desktop/image_1.jpg", + "/Users/basilshaji/Desktop/image_2.jpg", + "/Users/basilshaji/Desktop/image_3.jpg", + "/Users/basilshaji/Desktop/image_4.jpg", + "/Users/basilshaji/Desktop/image_5.jpg", + "/Users/basilshaji/Desktop/image_6.jpg", + ], + description=' "This online deployment proves the effectiveness and efficient function of the machine learning model in identifying lung cancer nodules. The implementation of YOLO for core detection tasks is employed that is an efficient and accurate algorithm for object detection. Through the precise hyper-parameter tuning process, the model proposed in this paper has given an impressive boost in the performance. Moreover, the model uses Retinanet algorithm which is recognized as the powerful tool effective in dense object detection. In an attempt to enhance the model’s performance, the backbone of this architecture consists of a Feature Pyramid Network (FPN). The FPN plays an important role in boosting the model’s capacity in recognizing objects in different scales through the construction of high semantic feature map in different resolutions. In conclusion, this deployment encompasses YOLOv5, hyperparameter optimization, Retinanet, and FPN as one of the most effective and modern solutions for the detection of lung cancer nodules." ~ Basil Shaji 😇', + live=False, +) + +interface.launch() diff --git a/app11.py b/app11.py new file mode 100644 index 0000000000000000000000000000000000000000..dd9086b8b5ef2179864f70b1b8fe92f7bc44b188 --- /dev/null +++ b/app11.py @@ -0,0 +1,67 @@ +import gradio as gr +import cv2 +import requests +import os +import torch +from yolov5.models.experimental import attempt_load +from yolov5.utils.general import non_max_suppression +import yolov5.utils.plots +import yolov5.utils.plots +dir(yolov5.utils.plots) + +file_urls = [ + "https://www.dropbox.com/scl/fi/n3bs5xnl2kanqmwv483k3/1_jpg.rf.4a59a63d0a7339d280dd18ef3c2e675a.jpg?rlkey=4n9dnls1byb4wm54ycxzx3ovi&st=ue5xv8yx&dl=0", + "https://www.dropbox.com/scl/fi/asrmao4b4fpsrhqex8kog/2_jpg.rf.b87583d95aa220d4b7b532ae1948e7b7.jpg?rlkey=jkmux5jjy8euzhxizupdmpesb&st=v3ld14tx&dl=0", + "https://www.dropbox.com/scl/fi/fi0e8zxqqy06asnu0robz/3_jpg.rf.d2932cce7e88c2675e300ececf9f1b82.jpg?rlkey=hfdqwxkxetabe38ukzbb39pl5&st=ga1uouhj&dl=0", + "https://www.dropbox.com/scl/fi/ruobyat1ld1c33ch5yjpv/4_jpg.rf.3395c50b4db0ec0ed3448276965b2459.jpg?rlkey=j1m4qa0pmdh3rlr344v82u3am&st=lex8h3qi&dl=0", + "https://www.dropbox.com/scl/fi/ok3izk4jj1pg6psxja3aj/5_jpg.rf.62f3dc64b6c894fbb165d8f6e2ee1382.jpg?rlkey=euu16z8fd8u8za4aflvu5qg4v&st=pwno39nc&dl=0", + "https://www.dropbox.com/scl/fi/8r1fpwxkwq7c2i6ky6qv5/10_jpg.rf.c1785c33dd3552e860bf043c2fd0a379.jpg?rlkey=fcw41ppgzu0ao7xo6ijbpdi4c&st=to2udvxb&dl=0", + "https://www.dropbox.com/scl/fi/ihiid7hbz1vvaoqrstwa5/7_jpg.rf.dfc30f9dc198cf6697d9023ac076e822.jpg?rlkey=yh67p4ex52wn9t0bfw0jr77ef&st=02qw80xa&dl=0", +] + +def download_file(url, save_name): + if not os.path.exists(save_name): + file = requests.get(url) + open(save_name,'wb').write(file.content) + +# Download files +for i, url in enumerate(file_urls): + if "mp4" in url: + download_file(url, "video.mp4") + else: + download_file(url, f"image_{i}.jpg") + +# Load YOLOv5 model +model_path = "best.pt" +model = attempt_load(model_path, device=torch.device('cpu')) + +def show_preds_image(image_path): + img0 = cv2.imread(image_path) # Open image + + # Inference + results = model(img0) # Pass image to model + + # Process detections + for i, det in enumerate(results.pred[0]): + # Draw bounding boxes + plot_one_box(det.cpu().numpy(), img0, color=(0, 0, 255), line_thickness=2) + + return cv2.cvtColor(img0, cv2.COLOR_BGR2RGB) + +inputs_image = [ + gr.inputs.Image(type="file", label="Input Image"), +] +outputs_image = [ + gr.outputs.Image(type="numpy", label="Output Image"), +] + +interface_image = gr.Interface( + fn=show_preds_image, + inputs=inputs_image, + outputs=outputs_image, + title="YOLOv5 Object Detection", + examples=[["image_0.jpg"], ["image_1.jpg"]], + live=False, +) + +interface_image.launch() diff --git a/best.pt b/best.pt new file mode 100644 index 0000000000000000000000000000000000000000..4c27920247da290722666ae37615301ece65457a --- /dev/null +++ b/best.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:84a35b45b853290e0e700e0fa989b5e7e0c84365876e86572407204f2bc87ae0 +size 369979642 diff --git a/flagged/Input Image/26730035dd285fd05dba/image_1.jpg b/flagged/Input Image/26730035dd285fd05dba/image_1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..ad23f8b24c43e7a74da8e7fdc71df74c056fbc2f Binary files /dev/null and b/flagged/Input Image/26730035dd285fd05dba/image_1.jpg differ diff --git a/flagged/Output Image/9a5b477097058958d1b7/image.png b/flagged/Output Image/9a5b477097058958d1b7/image.png new file mode 100644 index 0000000000000000000000000000000000000000..eeda460ddbec1a899e97961912df675499d985ff Binary files /dev/null and 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +Dropbox - 4_jpg.rf.3395c50b4db0ec0ed3448276965b2459.jpg - Simplify your life + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/image_4.jpg b/image_4.jpg new file mode 100644 index 0000000000000000000000000000000000000000..5565f87856abc86ab97443bb2c3d69d274a15ebb --- /dev/null +++ b/image_4.jpg @@ -0,0 +1,532 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +Dropbox - 5_jpg.rf.62f3dc64b6c894fbb165d8f6e2ee1382.jpg - Simplify your life + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/image_5.jpg b/image_5.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e0c931c9065650d19574577c7101c41cf0e5df56 --- /dev/null +++ b/image_5.jpg @@ -0,0 +1,532 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +Dropbox - 10_jpg.rf.c1785c33dd3552e860bf043c2fd0a379.jpg - Simplify your life + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/image_6.jpg b/image_6.jpg new file mode 100644 index 0000000000000000000000000000000000000000..39621c9fcd868dc296a666bdb0c563ada731c61e --- /dev/null +++ b/image_6.jpg @@ -0,0 +1,532 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +Dropbox - 7_jpg.rf.dfc30f9dc198cf6697d9023ac076e822.jpg - Simplify your life + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/pre-requirements.txt b/pre-requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..16f84397da69c0a44a55a5c0a10e8c7966d4fe88 --- /dev/null +++ b/pre-requirements.txt @@ -0,0 +1,2 @@ +-e +yolov5 \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..65801f70486bda7ee5acc900b5de1f8fd8917d9d --- /dev/null +++ b/requirements.txt @@ -0,0 +1,50 @@ +# YOLOv5 requirements +# Usage: pip install -r requirements.txt + +# Base ------------------------------------------------------------------------ +gitpython>=3.1.30 +matplotlib>=3.3 +numpy>=1.23.5 +opencv-python>=4.1.1 +pillow>=10.3.0 +psutil # system resources +PyYAML>=5.3.1 +requests>=2.32.0 +scipy>=1.4.1 +wget +thop>=0.1.1 # FLOPs computation +torch>=1.8.0 # see https://pytorch.org/get-started/locally (recommended) +torchvision>=0.9.0 +tqdm>=4.64.0 +ultralytics>=8.2.34 # https://ultralytics.com +# protobuf<=3.20.1 # https://github.com/ultralytics/yolov5/issues/8012 + +# Logging --------------------------------------------------------------------- +# tensorboard>=2.4.1 +# clearml>=1.2.0 +# comet + +# Plotting -------------------------------------------------------------------- +pandas>=1.1.4 +seaborn>=0.11.0 + +# Export ---------------------------------------------------------------------- +# coremltools>=6.0 # CoreML export +# onnx>=1.10.0 # ONNX export +# onnx-simplifier>=0.4.1 # ONNX simplifier +# nvidia-pyindex # TensorRT export +# nvidia-tensorrt # TensorRT export +# scikit-learn<=1.1.2 # CoreML quantization +# tensorflow>=2.4.0,<=2.13.1 # TF exports (-cpu, -aarch64, -macos) +# tensorflowjs>=3.9.0 # TF.js export +# openvino-dev>=2023.0 # OpenVINO export + +# Deploy ---------------------------------------------------------------------- +setuptools>=65.5.1 # Snyk vulnerability fix +# tritonclient[all]~=2.24.0 + +# Extras ---------------------------------------------------------------------- +# ipython # interactive notebook +# mss # screenshots +# albumentations>=1.0.3 +# pycocotools>=2.0.6 # COCO mAP diff --git a/yolov5/.dockerignore b/yolov5/.dockerignore new file mode 100644 index 0000000000000000000000000000000000000000..3b669254e7799c0460d6be8523ed15ef1d2c3ac6 --- /dev/null +++ b/yolov5/.dockerignore @@ -0,0 +1,222 @@ +# Repo-specific DockerIgnore ------------------------------------------------------------------------------------------- +.git +.cache +.idea +runs +output +coco +storage.googleapis.com + +data/samples/* +**/results*.csv +*.jpg + +# Neural Network weights ----------------------------------------------------------------------------------------------- +**/*.pt +**/*.pth +**/*.onnx +**/*.engine +**/*.mlmodel +**/*.torchscript +**/*.torchscript.pt +**/*.tflite +**/*.h5 +**/*.pb +*_saved_model/ +*_web_model/ +*_openvino_model/ + +# Below Copied From .gitignore ----------------------------------------------------------------------------------------- +# Below Copied From .gitignore ----------------------------------------------------------------------------------------- + + +# GitHub Python GitIgnore ---------------------------------------------------------------------------------------------- +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +wandb/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv* +venv*/ +ENV*/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + + +# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore ----------------------------------------------- + +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon +Icon? + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + + +# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff: +.idea/* +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/dictionaries +.html # Bokeh Plots +.pg # TensorFlow Frozen Graphs +.avi # videos + +# Sensitive or high-churn files: +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml + +# Gradle: +.idea/**/gradle.xml +.idea/**/libraries + +# CMake +cmake-build-debug/ +cmake-build-release/ + +# Mongo Explorer plugin: +.idea/**/mongoSettings.xml + +## File-based project format: +*.iws + +## Plugin-specific files: + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties diff --git a/yolov5/.gitattributes b/yolov5/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..dad4239ebad5b72917cbc4bba95206c1e55d519e --- /dev/null +++ b/yolov5/.gitattributes @@ -0,0 +1,2 @@ +# this drop notebooks from GitHub language stats +*.ipynb linguist-vendored diff --git a/yolov5/.github/ISSUE_TEMPLATE/bug-report.yml b/yolov5/.github/ISSUE_TEMPLATE/bug-report.yml new file mode 100644 index 0000000000000000000000000000000000000000..76b426a195e41a9a38701ebad9da4c0e7a8997c8 --- /dev/null +++ b/yolov5/.github/ISSUE_TEMPLATE/bug-report.yml @@ -0,0 +1,87 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +name: 🐛 Bug Report +# title: " " +description: Problems with YOLOv5 +labels: [bug, triage] +body: + - type: markdown + attributes: + value: | + Thank you for submitting a YOLOv5 🐛 Bug Report! + + - type: checkboxes + attributes: + label: Search before asking + description: > + Please search the [issues](https://github.com/ultralytics/yolov5/issues) to see if a similar bug report already exists. + options: + - label: > + I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and found no similar bug report. + required: true + + - type: dropdown + attributes: + label: YOLOv5 Component + description: | + Please select the part of YOLOv5 where you found the bug. + multiple: true + options: + - "Training" + - "Validation" + - "Detection" + - "Export" + - "PyTorch Hub" + - "Multi-GPU" + - "Evolution" + - "Integrations" + - "Other" + validations: + required: false + + - type: textarea + attributes: + label: Bug + description: Provide console output with error messages and/or screenshots of the bug. + placeholder: | + 💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response. + validations: + required: true + + - type: textarea + attributes: + label: Environment + description: Please specify the software and hardware you used to produce the bug. + placeholder: | + - YOLO: YOLOv5 🚀 v6.0-67-g60e42e1 torch 1.9.0+cu111 CUDA:0 (A100-SXM4-40GB, 40536MiB) + - OS: Ubuntu 20.04 + - Python: 3.9.0 + validations: + required: false + + - type: textarea + attributes: + label: Minimal Reproducible Example + description: > + When asking a question, people will be better able to provide help if you provide code that they can easily understand and use to **reproduce** the problem. + This is referred to by community members as creating a [minimal reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/). + placeholder: | + ``` + # Code to reproduce your issue here + ``` + validations: + required: false + + - type: textarea + attributes: + label: Additional + description: Anything else you would like to share? + + - type: checkboxes + attributes: + label: Are you willing to submit a PR? + description: > + (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/yolov5/pulls) (PR) to help improve YOLOv5 for everyone, especially if you have a good understanding of how to implement a fix or feature. + See the YOLOv5 [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started. + options: + - label: Yes I'd like to help by submitting a PR! diff --git a/yolov5/.github/ISSUE_TEMPLATE/config.yml b/yolov5/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000000000000000000000000000000000000..e23dfcf9c56b2aa610556a42e9e0f158683c637c --- /dev/null +++ b/yolov5/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,13 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +blank_issues_enabled: true +contact_links: + - name: 📄 Docs + url: https://docs.ultralytics.com/yolov5 + about: View Ultralytics YOLOv5 Docs + - name: 💬 Forum + url: https://community.ultralytics.com/ + about: Ask on Ultralytics Community Forum + - name: 🎧 Discord + url: https://ultralytics.com/discord + about: Ask on Ultralytics Discord diff --git a/yolov5/.github/ISSUE_TEMPLATE/feature-request.yml b/yolov5/.github/ISSUE_TEMPLATE/feature-request.yml new file mode 100644 index 0000000000000000000000000000000000000000..9282e62d2a5798c8823aadf32153f6a139882f6c --- /dev/null +++ b/yolov5/.github/ISSUE_TEMPLATE/feature-request.yml @@ -0,0 +1,52 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +name: 🚀 Feature Request +description: Suggest a YOLOv5 idea +# title: " " +labels: [enhancement] +body: + - type: markdown + attributes: + value: | + Thank you for submitting a YOLOv5 🚀 Feature Request! + + - type: checkboxes + attributes: + label: Search before asking + description: > + Please search the [issues](https://github.com/ultralytics/yolov5/issues) to see if a similar feature request already exists. + options: + - label: > + I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and found no similar feature requests. + required: true + + - type: textarea + attributes: + label: Description + description: A short description of your feature. + placeholder: | + What new feature would you like to see in YOLOv5? + validations: + required: true + + - type: textarea + attributes: + label: Use case + description: | + Describe the use case of your feature request. It will help us understand and prioritize the feature request. + placeholder: | + How would this feature be used, and who would use it? + + - type: textarea + attributes: + label: Additional + description: Anything else you would like to share? + + - type: checkboxes + attributes: + label: Are you willing to submit a PR? + description: > + (Optional) We encourage you to submit a [Pull Request](https://github.com/ultralytics/yolov5/pulls) (PR) to help improve YOLOv5 for everyone, especially if you have a good understanding of how to implement a fix or feature. + See the YOLOv5 [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started. + options: + - label: Yes I'd like to help by submitting a PR! diff --git a/yolov5/.github/ISSUE_TEMPLATE/question.yml b/yolov5/.github/ISSUE_TEMPLATE/question.yml new file mode 100644 index 0000000000000000000000000000000000000000..5b62af675e2a00a194096920402887b7bbbc31a0 --- /dev/null +++ b/yolov5/.github/ISSUE_TEMPLATE/question.yml @@ -0,0 +1,35 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +name: ❓ Question +description: Ask a YOLOv5 question +# title: " " +labels: [question] +body: + - type: markdown + attributes: + value: | + Thank you for asking a YOLOv5 ❓ Question! + + - type: checkboxes + attributes: + label: Search before asking + description: > + Please search the [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) to see if a similar question already exists. + options: + - label: > + I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions. + required: true + + - type: textarea + attributes: + label: Question + description: What is your question? + placeholder: | + 💡 ProTip! Include as much information as possible (screenshots, logs, tracebacks etc.) to receive the most helpful response. + validations: + required: true + + - type: textarea + attributes: + label: Additional + description: Anything else you would like to share? diff --git a/yolov5/.github/dependabot.yml b/yolov5/.github/dependabot.yml new file mode 100644 index 0000000000000000000000000000000000000000..061d61f1bc246e6985f224442f59eb985a0c4a02 --- /dev/null +++ b/yolov5/.github/dependabot.yml @@ -0,0 +1,27 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# Dependabot for package version updates +# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates + +version: 2 +updates: + - package-ecosystem: pip + directory: "/" + schedule: + interval: weekly + time: "04:00" + open-pull-requests-limit: 10 + reviewers: + - glenn-jocher + labels: + - dependencies + + - package-ecosystem: github-actions + directory: "/.github/workflows" + schedule: + interval: weekly + time: "04:00" + open-pull-requests-limit: 5 + reviewers: + - glenn-jocher + labels: + - dependencies diff --git a/yolov5/.github/workflows/ci-testing.yml b/yolov5/.github/workflows/ci-testing.yml new file mode 100644 index 0000000000000000000000000000000000000000..639d0449c2819544cbd47ed12a163b70aeb3ded9 --- /dev/null +++ b/yolov5/.github/workflows/ci-testing.yml @@ -0,0 +1,150 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# YOLOv5 Continuous Integration (CI) GitHub Actions tests + +name: YOLOv5 CI + +on: + push: + branches: [master] + pull_request: + branches: [master] + schedule: + - cron: "0 0 * * *" # runs at 00:00 UTC every day + workflow_dispatch: + +jobs: + Benchmarks: + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest] + python-version: ["3.11"] # requires python<=3.11 + model: [yolov5n] + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + cache: "pip" # cache pip dependencies + - name: Install requirements + run: | + python -m pip install --upgrade pip wheel + pip install -r requirements.txt coremltools openvino-dev "tensorflow-cpu<2.15.1" --extra-index-url https://download.pytorch.org/whl/cpu + yolo checks + pip list + - name: Benchmark DetectionModel + run: | + python benchmarks.py --data coco128.yaml --weights ${{ matrix.model }}.pt --img 320 --hard-fail 0.29 + - name: Benchmark SegmentationModel + run: | + python benchmarks.py --data coco128-seg.yaml --weights ${{ matrix.model }}-seg.pt --img 320 --hard-fail 0.22 + - name: Test predictions + run: | + python export.py --weights ${{ matrix.model }}-cls.pt --include onnx --img 224 + python detect.py --weights ${{ matrix.model }}.onnx --img 320 + python segment/predict.py --weights ${{ matrix.model }}-seg.onnx --img 320 + python classify/predict.py --weights ${{ matrix.model }}-cls.onnx --img 224 + + Tests: + timeout-minutes: 60 + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, windows-latest, macos-14] # macos-latest bug https://github.com/ultralytics/yolov5/pull/9049 + python-version: ["3.11"] + model: [yolov5n] + include: + - os: ubuntu-latest + python-version: "3.8" # torch 1.8.0 requires python >=3.6, <=3.8 + model: yolov5n + torch: "1.8.0" # min torch version CI https://pypi.org/project/torchvision/ + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + cache: "pip" # caching pip dependencies + - name: Install requirements + run: | + python -m pip install --upgrade pip wheel + torch="" + if [ "${{ matrix.torch }}" == "1.8.0" ]; then + torch="torch==1.8.0 torchvision==0.9.0" + fi + pip install -r requirements.txt $torch --extra-index-url https://download.pytorch.org/whl/cpu + shell: bash # for Windows compatibility + - name: Check environment + run: | + yolo checks + pip list + - name: Test detection + shell: bash # for Windows compatibility + run: | + # export PYTHONPATH="$PWD" # to run '$ python *.py' files in subdirectories + m=${{ matrix.model }} # official weights + b=runs/train/exp/weights/best # best.pt checkpoint + python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device cpu # train + for d in cpu; do # devices + for w in $m $b; do # weights + python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val + python detect.py --imgsz 64 --weights $w.pt --device $d # detect + done + done + python hubconf.py --model $m # hub + # python models/tf.py --weights $m.pt # build TF model + python models/yolo.py --cfg $m.yaml # build PyTorch model + python export.py --weights $m.pt --img 64 --include torchscript # export + python - < GitHub Actions error for ${{ github.workflow }} ❌\n\n\n*Repository:* https://github.com/${{ github.repository }}\n*Action:* https://github.com/${{ github.repository }}/actions/runs/${{ github.run_id }}\n*Author:* ${{ github.actor }}\n*Event:* ${{ github.event_name }}\n"} + env: + SLACK_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK_URL_YOLO }} diff --git a/yolov5/.github/workflows/cla.yml b/yolov5/.github/workflows/cla.yml new file mode 100644 index 0000000000000000000000000000000000000000..004acaf6461aafc48e25d67ddada2c1a4a4c9819 --- /dev/null +++ b/yolov5/.github/workflows/cla.yml @@ -0,0 +1,44 @@ +# Ultralytics YOLO 🚀, AGPL-3.0 license +# Ultralytics Contributor License Agreement (CLA) action https://docs.ultralytics.com/help/CLA +# This workflow automatically requests Pull Requests (PR) authors to sign the Ultralytics CLA before PRs can be merged + +name: CLA Assistant +on: + issue_comment: + types: + - created + pull_request_target: + types: + - reopened + - opened + - synchronize + +permissions: + actions: write + contents: write + pull-requests: write + statuses: write + +jobs: + CLA: + if: github.repository == 'ultralytics/yolov5' + runs-on: ubuntu-latest + steps: + - name: CLA Assistant + if: (github.event.comment.body == 'recheck' || github.event.comment.body == 'I have read the CLA Document and I sign the CLA') || github.event_name == 'pull_request_target' + uses: contributor-assistant/github-action@v2.4.0 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + # Must be repository secret PAT + PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }} + with: + path-to-signatures: "signatures/version1/cla.json" + path-to-document: "https://docs.ultralytics.com/help/CLA" # CLA document + # Branch must not be protected + branch: "cla-signatures" + allowlist: dependabot[bot],github-actions,[pre-commit*,pre-commit*,bot* + + remote-organization-name: ultralytics + remote-repository-name: cla + custom-pr-sign-comment: "I have read the CLA Document and I sign the CLA" + custom-allsigned-prcomment: All Contributors have signed the CLA. ✅ diff --git a/yolov5/.github/workflows/codeql-analysis.yml b/yolov5/.github/workflows/codeql-analysis.yml new file mode 100644 index 0000000000000000000000000000000000000000..77054fd0d7c504b5307e61740a0bba7aa2a4cd3d --- /dev/null +++ b/yolov5/.github/workflows/codeql-analysis.yml @@ -0,0 +1,56 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# This action runs GitHub's industry-leading static analysis engine, CodeQL, against a repository's source code to find security vulnerabilities. +# https://github.com/github/codeql-action + +name: "CodeQL" + +on: + schedule: + - cron: "0 0 1 * *" # Runs at 00:00 UTC on the 1st of every month + workflow_dispatch: + +jobs: + analyze: + name: Analyze + runs-on: ubuntu-latest + + strategy: + fail-fast: false + matrix: + language: ["python"] + # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python' ] + # Learn more: + # https://docs.github.com/en/free-pro-team@latest/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#changing-the-languages-that-are-analyzed + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + # Initializes the CodeQL tools for scanning. + - name: Initialize CodeQL + uses: github/codeql-action/init@v3 + with: + languages: ${{ matrix.language }} + # If you wish to specify custom queries, you can do so here or in a config file. + # By default, queries listed here will override any specified in a config file. + # Prefix the list here with "+" to use these queries and those in the config file. + # queries: ./path/to/local/query, your-org/your-repo/queries@main + + # Autobuild attempts to build any compiled languages (C/C++, C#, or Java). + # If this step fails, then you should remove it and run the build manually (see below) + - name: Autobuild + uses: github/codeql-action/autobuild@v3 + + # ℹ️ Command-line programs to run using the OS shell. + # 📚 https://git.io/JvXDl + + # ✏️ If the Autobuild fails above, remove it and uncomment the following three lines + # and modify them (or add more) to build your code if your project + # uses a compiled language + + #- run: | + # make bootstrap + # make release + + - name: Perform CodeQL Analysis + uses: github/codeql-action/analyze@v3 diff --git a/yolov5/.github/workflows/docker.yml b/yolov5/.github/workflows/docker.yml new file mode 100644 index 0000000000000000000000000000000000000000..2cd1c13f78b0cd7ba7b984fba20ca5c2f54f0577 --- /dev/null +++ b/yolov5/.github/workflows/docker.yml @@ -0,0 +1,60 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# Builds ultralytics/yolov5:latest images on DockerHub https://hub.docker.com/r/ultralytics/yolov5 + +name: Publish Docker Images + +on: + push: + branches: [master] + workflow_dispatch: + +jobs: + docker: + if: github.repository == 'ultralytics/yolov5' + name: Push Docker image to Docker Hub + runs-on: ubuntu-latest + steps: + - name: Checkout repo + uses: actions/checkout@v4 + with: + fetch-depth: 0 # copy full .git directory to access full git history in Docker images + + - name: Set up QEMU + uses: docker/setup-qemu-action@v3 + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v3 + + - name: Login to Docker Hub + uses: docker/login-action@v3 + with: + username: ${{ secrets.DOCKERHUB_USERNAME }} + password: ${{ secrets.DOCKERHUB_TOKEN }} + + - name: Build and push arm64 image + uses: docker/build-push-action@v6 + continue-on-error: true + with: + context: . + platforms: linux/arm64 + file: utils/docker/Dockerfile-arm64 + push: true + tags: ultralytics/yolov5:latest-arm64 + + - name: Build and push CPU image + uses: docker/build-push-action@v6 + continue-on-error: true + with: + context: . + file: utils/docker/Dockerfile-cpu + push: true + tags: ultralytics/yolov5:latest-cpu + + - name: Build and push GPU image + uses: docker/build-push-action@v6 + continue-on-error: true + with: + context: . + file: utils/docker/Dockerfile + push: true + tags: ultralytics/yolov5:latest diff --git a/yolov5/.github/workflows/format.yml b/yolov5/.github/workflows/format.yml new file mode 100644 index 0000000000000000000000000000000000000000..ee176abee99670bd586a80e83021d0420d661905 --- /dev/null +++ b/yolov5/.github/workflows/format.yml @@ -0,0 +1,28 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# Ultralytics Actions https://github.com/ultralytics/actions +# This workflow automatically formats code and documentation in PRs to official Ultralytics standards + +name: Ultralytics Actions + +on: + push: + branches: [main, master] + pull_request_target: + branches: [main, master] + +jobs: + format: + runs-on: ubuntu-latest + steps: + - name: Run Ultralytics Formatting + uses: ultralytics/actions@main + with: + token: ${{ secrets.GITHUB_TOKEN }} # automatically generated, do not modify + python: true # format Python code and docstrings + markdown: true # format Markdown + prettier: true # format YAML + spelling: true # check spelling + links: false # check broken links + summary: true # print PR summary with GPT4 (requires 'openai_api_key' or 'openai_azure_api_key' and 'openai_azure_endpoint') + openai_azure_api_key: ${{ secrets.OPENAI_AZURE_API_KEY }} + openai_azure_endpoint: ${{ secrets.OPENAI_AZURE_ENDPOINT }} diff --git a/yolov5/.github/workflows/greetings.yml b/yolov5/.github/workflows/greetings.yml new file mode 100644 index 0000000000000000000000000000000000000000..212211d2445e1622f2f97aba40c11823ff989107 --- /dev/null +++ b/yolov5/.github/workflows/greetings.yml @@ -0,0 +1,65 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +name: Greetings + +on: + pull_request_target: + types: [opened] + issues: + types: [opened] + +jobs: + greeting: + runs-on: ubuntu-latest + steps: + - uses: actions/first-interaction@v1 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + pr-message: | + 👋 Hello @${{ github.actor }}, thank you for submitting a YOLOv5 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to: + + - ✅ Verify your PR is **up-to-date** with `ultralytics/yolov5` `master` branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by running `git pull` and `git merge master` locally. + - ✅ Verify all YOLOv5 Continuous Integration (CI) **checks are passing**. + - ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee + + issue-message: | + 👋 Hello @${{ github.actor }}, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ [Tutorials](https://docs.ultralytics.com/yolov5/) to get started, where you can find quickstart guides for simple tasks like [Custom Data Training](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/) all the way to advanced concepts like [Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution/). + + If this is a 🐛 Bug Report, please provide a **minimum reproducible example** to help us debug it. + + If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our [Tips for Best Training Results](https://docs.ultralytics.com/guides/model-training-tips//). + + ## Requirements + + [**Python>=3.8.0**](https://www.python.org/) with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). To get started: + ```bash + git clone https://github.com/ultralytics/yolov5 # clone + cd yolov5 + pip install -r requirements.txt # install + ``` + + ## Environments + + YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): + + - **Notebooks** with free GPU: Run on Gradient Open In Colab Open In Kaggle + - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/) + - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/) + - **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) Docker Pulls + + ## Status + + YOLOv5 CI + + If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit. + + ## Introducing YOLOv8 🚀 + + We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - [YOLOv8](https://github.com/ultralytics/ultralytics) 🚀! + + Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects. + + Check out our [YOLOv8 Docs](https://docs.ultralytics.com/) for details and get started with: + ```bash + pip install ultralytics + ``` diff --git a/yolov5/.github/workflows/links.yml b/yolov5/.github/workflows/links.yml new file mode 100644 index 0000000000000000000000000000000000000000..80e2a48be71f1a95d13cdeac2f71977fb66c8582 --- /dev/null +++ b/yolov5/.github/workflows/links.yml @@ -0,0 +1,71 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# Continuous Integration (CI) GitHub Actions tests broken link checker using https://github.com/lycheeverse/lychee +# Ignores the following status codes to reduce false positives: +# - 403(OpenVINO, 'forbidden') +# - 429(Instagram, 'too many requests') +# - 500(Zenodo, 'cached') +# - 502(Zenodo, 'bad gateway') +# - 999(LinkedIn, 'unknown status code') + +name: Check Broken links + +on: + workflow_dispatch: + schedule: + - cron: "0 0 * * *" # runs at 00:00 UTC every day + +jobs: + Links: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Download and install lychee + run: | + LYCHEE_URL=$(curl -s https://api.github.com/repos/lycheeverse/lychee/releases/latest | grep "browser_download_url" | grep "x86_64-unknown-linux-gnu.tar.gz" | cut -d '"' -f 4) + curl -L $LYCHEE_URL -o lychee.tar.gz + tar xzf lychee.tar.gz + sudo mv lychee /usr/local/bin + + - name: Test Markdown and HTML links with retry + uses: nick-invision/retry@v3 + with: + timeout_minutes: 5 + retry_wait_seconds: 60 + max_attempts: 3 + command: | + lychee \ + --scheme 'https' \ + --timeout 60 \ + --insecure \ + --accept 403,429,500,502,999 \ + --exclude-all-private \ + --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \ + --exclude-path '**/ci.yaml' \ + --github-token ${{ secrets.GITHUB_TOKEN }} \ + './**/*.md' \ + './**/*.html' + + - name: Test Markdown, HTML, YAML, Python and Notebook links with retry + if: github.event_name == 'workflow_dispatch' + uses: nick-invision/retry@v3 + with: + timeout_minutes: 5 + retry_wait_seconds: 60 + max_attempts: 3 + command: | + lychee \ + --scheme 'https' \ + --timeout 60 \ + --insecure \ + --accept 429,999 \ + --exclude-all-private \ + --exclude 'https?://(www\.)?(linkedin\.com|twitter\.com|instagram\.com|kaggle\.com|fonts\.gstatic\.com|url\.com)' \ + --exclude-path '**/ci.yaml' \ + --github-token ${{ secrets.GITHUB_TOKEN }} \ + './**/*.md' \ + './**/*.html' \ + './**/*.yml' \ + './**/*.yaml' \ + './**/*.py' \ + './**/*.ipynb' diff --git a/yolov5/.github/workflows/merge-main-into-prs.yml b/yolov5/.github/workflows/merge-main-into-prs.yml new file mode 100644 index 0000000000000000000000000000000000000000..2cd4b028c8b5379fef11ce2cf3972eb1d831ecc2 --- /dev/null +++ b/yolov5/.github/workflows/merge-main-into-prs.yml @@ -0,0 +1,56 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# Automatically merges repository 'main' branch into all open PRs to keep them up-to-date +# Action runs on updates to main branch so when one PR merges to main all others update + +name: Merge main into PRs + +on: + workflow_dispatch: + push: + branches: + - main + - master + +jobs: + Merge: + if: github.repository == 'ultralytics/yolov5' + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v4 + with: + fetch-depth: 0 + - uses: actions/setup-python@v5 + with: + python-version: "3.11" + cache: "pip" # caching pip dependencies + - name: Install requirements + run: | + pip install pygithub + - name: Merge main into PRs + shell: python + run: | + from github import Github + import os + + # Authenticate with the GitHub Token + g = Github(os.getenv('GITHUB_TOKEN')) + + # Get the repository dynamically + repo = g.get_repo(os.getenv('GITHUB_REPOSITORY')) + + # List all open pull requests + open_pulls = repo.get_pulls(state='open', sort='created') + + for pr in open_pulls: + # Compare PR head with main to see if it's behind + try: + # Merge main into the PR branch + success = pr.update_branch() + assert success, "Branch update failed" + print(f"Merged 'master' into PR #{pr.number} ({pr.head.ref}) successfully.") + except Exception as e: + print(f"Could not merge 'master' into PR #{pr.number} ({pr.head.ref}): {e}") + env: + GITHUB_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }} + GITHUB_REPOSITORY: ${{ github.repository }} diff --git a/yolov5/.github/workflows/stale.yml b/yolov5/.github/workflows/stale.yml new file mode 100644 index 0000000000000000000000000000000000000000..374bc01ab0b7fb432361a2a8d3c73049fb1847c6 --- /dev/null +++ b/yolov5/.github/workflows/stale.yml @@ -0,0 +1,47 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +name: Close stale issues +on: + schedule: + - cron: "0 0 * * *" # Runs at 00:00 UTC every day + +jobs: + stale: + runs-on: ubuntu-latest + steps: + - uses: actions/stale@v9 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + + stale-issue-message: | + 👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help. + + For additional resources and information, please see the links below: + + - **Docs**: https://docs.ultralytics.com + - **HUB**: https://hub.ultralytics.com + - **Community**: https://community.ultralytics.com + + Feel free to inform us of any other **issues** you discover or **feature requests** that come to mind in the future. Pull Requests (PRs) are also always welcomed! + + Thank you for your contributions to YOLO 🚀 and Vision AI ⭐ + + stale-pr-message: | + 👋 Hello there! We wanted to let you know that we've decided to close this pull request due to inactivity. We appreciate the effort you put into contributing to our project, but unfortunately, not all contributions are suitable or aligned with our product roadmap. + + We hope you understand our decision, and please don't let it discourage you from contributing to open source projects in the future. We value all of our community members and their contributions, and we encourage you to keep exploring new projects and ways to get involved. + + For additional resources and information, please see the links below: + + - **Docs**: https://docs.ultralytics.com + - **HUB**: https://hub.ultralytics.com + - **Community**: https://community.ultralytics.com + + Thank you for your contributions to YOLO 🚀 and Vision AI ⭐ + + days-before-issue-stale: 30 + days-before-issue-close: 10 + days-before-pr-stale: 90 + days-before-pr-close: 30 + exempt-issue-labels: "documentation,tutorial,TODO" + operations-per-run: 300 # The maximum number of operations per run, used to control rate limiting. diff --git a/yolov5/.gitignore b/yolov5/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..6bcedfac610d86354e51c3719bf48bd149f644a2 --- /dev/null +++ b/yolov5/.gitignore @@ -0,0 +1,257 @@ +# Repo-specific GitIgnore ---------------------------------------------------------------------------------------------- +*.jpg +*.jpeg +*.png +*.bmp +*.tif +*.tiff +*.heic +*.JPG +*.JPEG +*.PNG +*.BMP +*.TIF +*.TIFF +*.HEIC +*.mp4 +*.mov +*.MOV +*.avi +*.data +*.json +*.cfg +!setup.cfg +!cfg/yolov3*.cfg + +storage.googleapis.com +runs/* +data/* +data/images/* +!data/*.yaml +!data/hyps +!data/scripts +!data/images +!data/images/zidane.jpg +!data/images/bus.jpg +!data/*.sh + +results*.csv + +# Datasets ------------------------------------------------------------------------------------------------------------- +coco/ +coco128/ +VOC/ + +# MATLAB GitIgnore ----------------------------------------------------------------------------------------------------- +*.m~ +*.mat +!targets*.mat + +# Neural Network weights ----------------------------------------------------------------------------------------------- +*.weights +*.pt +*.pb +*.onnx +*.engine +*.mlmodel +*.torchscript +*.tflite +*.h5 +*_saved_model/ +*_web_model/ +*_openvino_model/ +*_paddle_model/ +darknet53.conv.74 +yolov3-tiny.conv.15 + +# GitHub Python GitIgnore ---------------------------------------------------------------------------------------------- +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +/wandb/ +.installed.cfg +*.egg + + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv* +venv*/ +ENV*/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + + +# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore ----------------------------------------------- + +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon +Icon? + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + + +# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff: +.idea/* +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/dictionaries +.html # Bokeh Plots +.pg # TensorFlow Frozen Graphs +.avi # videos + +# Sensitive or high-churn files: +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml + +# Gradle: +.idea/**/gradle.xml +.idea/**/libraries + +# CMake +cmake-build-debug/ +cmake-build-release/ + +# Mongo Explorer plugin: +.idea/**/mongoSettings.xml + +## File-based project format: +*.iws + +## Plugin-specific files: + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties diff --git a/yolov5/CITATION.cff b/yolov5/CITATION.cff new file mode 100644 index 0000000000000000000000000000000000000000..c277230d922f869978cedaabc214d5236c6cb191 --- /dev/null +++ b/yolov5/CITATION.cff @@ -0,0 +1,14 @@ +cff-version: 1.2.0 +preferred-citation: + type: software + message: If you use YOLOv5, please cite it as below. + authors: + - family-names: Jocher + given-names: Glenn + orcid: "https://orcid.org/0000-0001-5950-6979" + title: "YOLOv5 by Ultralytics" + version: 7.0 + doi: 10.5281/zenodo.3908559 + date-released: 2020-5-29 + license: AGPL-3.0 + url: "https://github.com/ultralytics/yolov5" diff --git a/yolov5/CONTRIBUTING.md b/yolov5/CONTRIBUTING.md new file mode 100644 index 0000000000000000000000000000000000000000..556c554a2514a2cb3969bbaadca0e63b5587eda3 --- /dev/null +++ b/yolov5/CONTRIBUTING.md @@ -0,0 +1,76 @@ +## Contributing to YOLOv5 🚀 + +We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible, whether it's: + +- Reporting a bug +- Discussing the current state of the code +- Submitting a fix +- Proposing a new feature +- Becoming a maintainer + +YOLOv5 works so well due to our combined community effort, and for every small improvement you contribute you will be helping push the frontiers of what's possible in AI 😃! + +## Submitting a Pull Request (PR) 🛠️ + +Submitting a PR is easy! This example shows how to submit a PR for updating `requirements.txt` in 4 steps: + +### 1. Select File to Update + +Select `requirements.txt` to update by clicking on it in GitHub. + +

PR_step1

+ +### 2. Click 'Edit this file' + +The button is in the top-right corner. + +

PR_step2

+ +### 3. Make Changes + +Change the `matplotlib` version from `3.2.2` to `3.3`. + +

PR_step3

+ +### 4. Preview Changes and Submit PR + +Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch** for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose changes** button. All done, your PR is now submitted to YOLOv5 for review and approval 😃! + +

PR_step4

+ +### PR recommendations + +To allow your work to be integrated as seamlessly as possible, we advise you to: + +- ✅ Verify your PR is **up-to-date** with `ultralytics/yolov5` `master` branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by running `git pull` and `git merge master` locally. + +

Screenshot 2022-08-29 at 22 47 15

+ +- ✅ Verify all YOLOv5 Continuous Integration (CI) **checks are passing**. + +

Screenshot 2022-08-29 at 22 47 03

+ +- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee + +## Submitting a Bug Report 🐛 + +If you spot a problem with YOLOv5 please submit a Bug Report! + +For us to start investigating a possible problem we need to be able to reproduce it ourselves first. We've created a few short guidelines below to help users provide what we need to get started. + +When asking a question, people will be better able to provide help if you provide **code** that they can easily understand and use to **reproduce** the problem. This is referred to by community members as creating a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/). Your code that reproduces the problem should be: + +- ✅ **Minimal** – Use as little code as possible that still produces the same problem +- ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself +- ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem + +In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code should be: + +- ✅ **Current** – Verify that your code is up-to-date with the current GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new copy to ensure your problem has not already been resolved by previous commits. +- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️. + +If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 **Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and provide a [minimum reproducible example](https://docs.ultralytics.com/help/minimum_reproducible_example/) to help us better understand and diagnose your problem. + +## License + +By contributing, you agree that your contributions will be licensed under the [AGPL-3.0 license](https://choosealicense.com/licenses/agpl-3.0/) diff --git a/yolov5/LICENSE b/yolov5/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..be3f7b28e564e7dd05eaf59d64adba1a4065ac0e --- /dev/null +++ b/yolov5/LICENSE @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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+

+ + + +

+ +[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/) + +
+ YOLOv5 CI + YOLOv5 Citation + Docker Pulls + Discord Ultralytics Forums +
+ Run on Gradient + Open In Colab + Open In Kaggle +
+
+ +YOLOv5 🚀 is the world's most loved vision AI, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development. + +We hope that the resources here will help you get the most out of YOLOv5. Please browse the YOLOv5 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions! + +To request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license). + +
+ Ultralytics GitHub + + Ultralytics LinkedIn + + Ultralytics Twitter + + Ultralytics YouTube + + Ultralytics TikTok + + Ultralytics BiliBili + + Ultralytics Discord +
+ +
+
+ +##
YOLOv8 🚀 NEW
+ +We are thrilled to announce the launch of Ultralytics YOLOv8 🚀, our NEW cutting-edge, state-of-the-art (SOTA) model released at **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image classification tasks. + +See the [YOLOv8 Docs](https://docs.ultralytics.com) for details and get started with: + +[![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics) + +```bash +pip install ultralytics +``` + +
+ + +
+ +##
Documentation
+ +See the [YOLOv5 Docs](https://docs.ultralytics.com/yolov5) for full documentation on training, testing and deployment. See below for quickstart examples. + +
+Install + +Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.8.0**](https://www.python.org/) environment, including [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). + +```bash +git clone https://github.com/ultralytics/yolov5 # clone +cd yolov5 +pip install -r requirements.txt # install +``` + +
+ +
+Inference + +YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). + +```python +import torch + +# Model +model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, custom + +# Images +img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list + +# Inference +results = model(img) + +# Results +results.print() # or .show(), .save(), .crop(), .pandas(), etc. +``` + +
+ +
+Inference with detect.py + +`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. + +```bash +python detect.py --weights yolov5s.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/LNwODJXcvt4' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream +``` + +
+ +
+Training + +The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) results. [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) times faster). Use the largest `--batch-size` possible, or pass `--batch-size -1` for YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB. + +```bash +python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 + yolov5s 64 + yolov5m 40 + yolov5l 24 + yolov5x 16 +``` + + + +
+ +
+Tutorials + +- [Train Custom Data](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data) 🚀 RECOMMENDED +- [Tips for Best Training Results](https://docs.ultralytics.com/guides/model-training-tips/) ☘️ +- [Multi-GPU Training](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) +- [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) 🌟 NEW +- [TFLite, ONNX, CoreML, TensorRT Export](https://docs.ultralytics.com/yolov5/tutorials/model_export) 🚀 +- [NVIDIA Jetson platform Deployment](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano) 🌟 NEW +- [Test-Time Augmentation (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation) +- [Model Ensembling](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling) +- [Model Pruning/Sparsity](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity) +- [Hyperparameter Evolution](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution) +- [Transfer Learning with Frozen Layers](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers) +- [Architecture Summary](https://docs.ultralytics.com/yolov5/tutorials/architecture_description) 🌟 NEW +- [Roboflow for Datasets, Labeling, and Active Learning](https://docs.ultralytics.com/yolov5/tutorials/roboflow_datasets_integration) +- [ClearML Logging](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) 🌟 NEW +- [YOLOv5 with Neural Magic's Deepsparse](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization) 🌟 NEW +- [Comet Logging](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration) 🌟 NEW + +
+ +##
Integrations
+ +
+ + +
+
+ +
+ + + + + + + + + + + +
+ +| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW | +| :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | +| Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv5 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet2) lets you save YOLOv5 models, resume training, and interactively visualise and debug predictions | Run YOLOv5 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) | + +##
Ultralytics HUB
+ +Experience seamless AI with [Ultralytics HUB](https://ultralytics.com/hub) ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now! + + + + +##
Why YOLOv5
+ +YOLOv5 has been designed to be super easy to get started and simple to learn. We prioritize real-world results. + +

+
+ YOLOv5-P5 640 Figure + +

+
+
+ Figure Notes + +- **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536. +- **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32. +- **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8. +- **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` + +
+ +### Pretrained Checkpoints + +| Model | size
(pixels) | mAPval
50-95 | mAPval
50 | Speed
CPU b1
(ms) | Speed
V100 b1
(ms) | Speed
V100 b32
(ms) | params
(M) | FLOPs
@640 (B) | +| ----------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- | +| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** | +| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 | +| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 | +| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 | +| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 | +| | | | | | | | | | +| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 | +| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 | +| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 | +| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 | +| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)
+ [TTA] | 1280
1536 | 55.0
**55.8** | 72.7
**72.7** | 3136
- | 26.2
- | 19.4
- | 140.7
- | 209.8
- | + +
+ Table Notes + +- All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml). +- **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` +- **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.
Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1` +- **TTA** [Test Time Augmentation](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation) includes reflection and scale augmentations.
Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` + +
+ +##
Segmentation
+ +Our new YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) instance segmentation models are the fastest and most accurate in the world, beating all current [SOTA benchmarks](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco). We've made them super simple to train, validate and deploy. See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v7.0) and visit our [YOLOv5 Segmentation Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) for quickstart tutorials. + +
+ Segmentation Checkpoints + +
+ + +
+ +We trained YOLOv5 segmentations models on COCO for 300 epochs at image size 640 using A100 GPUs. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) notebooks for easy reproducibility. + +| Model | size
(pixels) | mAPbox
50-95 | mAPmask
50-95 | Train time
300 epochs
A100 (hours) | Speed
ONNX CPU
(ms) | Speed
TRT A100
(ms) | params
(M) | FLOPs
@640 (B) | +| ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | --------------------------------------------- | ------------------------------ | ------------------------------ | ------------------ | ---------------------- | +| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** | +| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 | +| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 | +| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 | +| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 | + +- All checkpoints are trained to 300 epochs with SGD optimizer with `lr0=0.01` and `weight_decay=5e-5` at image size 640 and all default settings.
Runs logged to https://wandb.ai/glenn-jocher/YOLOv5_v70_official +- **Accuracy** values are for single-model single-scale on COCO dataset.
Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt` +- **Speed** averaged over 100 inference images using a [Colab Pro](https://colab.research.google.com/signup) A100 High-RAM instance. Values indicate inference speed only (NMS adds about 1ms per image).
Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1` +- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`.
Reproduce by `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half` + +
+ +
+ Segmentation Usage Examples  Open In Colab + +### Train + +YOLOv5 segmentation training supports auto-download COCO128-seg segmentation dataset with `--data coco128-seg.yaml` argument and manual download of COCO-segments dataset with `bash data/scripts/get_coco.sh --train --val --segments` and then `python train.py --data coco.yaml`. + +```bash +# Single-GPU +python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 + +# Multi-GPU DDP +python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3 +``` + +### Val + +Validate YOLOv5s-seg mask mAP on COCO dataset: + +```bash +bash data/scripts/get_coco.sh --val --segments # download COCO val segments split (780MB, 5000 images) +python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate +``` + +### Predict + +Use pretrained YOLOv5m-seg.pt to predict bus.jpg: + +```bash +python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg +``` + +```python +model = torch.hub.load( + "ultralytics/yolov5", "custom", "yolov5m-seg.pt" +) # load from PyTorch Hub (WARNING: inference not yet supported) +``` + +| ![zidane](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![bus](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) | +| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | + +### Export + +Export YOLOv5s-seg model to ONNX and TensorRT: + +```bash +python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0 +``` + +
+ +##
Classification
+ +YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) brings support for classification model training, validation and deployment! See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v6.2) and visit our [YOLOv5 Classification Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) for quickstart tutorials. + +
+ Classification Checkpoints + +
+ +We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4xA100 instance, and we trained ResNet and EfficientNet models alongside with the same default training settings to compare. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) for easy reproducibility. + +| Model | size
(pixels) | acc
top1 | acc
top5 | Training
90 epochs
4xA100 (hours) | Speed
ONNX CPU
(ms) | Speed
TensorRT V100
(ms) | params
(M) | FLOPs
@224 (B) | +| -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ------------------------------ | ----------------------------------- | ------------------ | ---------------------- | +| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** | +| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 | +| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 | +| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 | +| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 | +| | | | | | | | | | +| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 | +| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 | +| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 | +| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 | +| | | | | | | | | | +| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 | +| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 | +| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 | +| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 | + +
+ Table Notes (click to expand) + +- All checkpoints are trained to 90 epochs with SGD optimizer with `lr0=0.001` and `weight_decay=5e-5` at image size 224 and all default settings.
Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2 +- **Accuracy** values are for single-model single-scale on [ImageNet-1k](https://www.image-net.org/index.php) dataset.
Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224` +- **Speed** averaged over 100 inference images using a Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM instance.
Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1` +- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`.
Reproduce by `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224` + +
+
+ +
+ Classification Usage Examples  Open In Colab + +### Train + +YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the `--data` argument. To start training on MNIST for example use `--data mnist`. + +```bash +# Single-GPU +python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128 + +# Multi-GPU DDP +python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3 +``` + +### Val + +Validate YOLOv5m-cls accuracy on ImageNet-1k dataset: + +```bash +bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) +python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate +``` + +### Predict + +Use pretrained YOLOv5s-cls.pt to predict bus.jpg: + +```bash +python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg +``` + +```python +model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt") # load from PyTorch Hub +``` + +### Export + +Export a group of trained YOLOv5s-cls, ResNet and EfficientNet models to ONNX and TensorRT: + +```bash +python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224 +``` + +
+ +##
Environments
+ +Get started in seconds with our verified environments. Click each icon below for details. + +
+ + + + + + + + + + + + + + + + + +
+ +##
Contribute
+ +We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started, and fill out the [YOLOv5 Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors! + + + + + + +##
License
+ +Ultralytics offers two licensing options to accommodate diverse use cases: + +- **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/licenses/) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) file for more details. +- **Enterprise License**: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through [Ultralytics Licensing](https://ultralytics.com/license). + +##
Contact
+ +For YOLOv5 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues), and join our [Discord](https://ultralytics.com/discord) community for questions and discussions! + +
+
+ Ultralytics GitHub + + Ultralytics LinkedIn + + Ultralytics Twitter + + Ultralytics YouTube + + Ultralytics TikTok + + Ultralytics BiliBili + + Ultralytics Discord +
+ +[tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation diff --git a/yolov5/README.zh-CN.md b/yolov5/README.zh-CN.md new file mode 100644 index 0000000000000000000000000000000000000000..cea3c26f28d10026f99db97acb6a3aea575ac554 --- /dev/null +++ b/yolov5/README.zh-CN.md @@ -0,0 +1,471 @@ +
+

+ + + +

+ +[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/) + +
+ YOLOv5 CI + YOLOv5 Citation + Docker Pulls +
+ Run on Gradient + Open In Colab + Open In Kaggle +
+
+ +YOLOv5 🚀 是世界上最受欢迎的视觉 AI，代表 Ultralytics 对未来视觉 AI 方法的开源研究，结合在数千小时的研究和开发中积累的经验教训和最佳实践。 + +我们希望这里的资源能帮助您充分利用 YOLOv5。请浏览 YOLOv5 文档 了解详细信息，在 GitHub 上提交问题以获得支持，并加入我们的 Discord 社区进行问题和讨论！ + +如需申请企业许可，请在 [Ultralytics Licensing](https://ultralytics.com/license) 处填写表格 + +
+ Ultralytics GitHub + + Ultralytics LinkedIn + + Ultralytics Twitter + + Ultralytics YouTube + + Ultralytics TikTok + + Ultralytics BiliBili + + Ultralytics Discord +
+
+ +##
YOLOv8 🚀 新品
+ +我们很高兴宣布 Ultralytics YOLOv8 🚀 的发布，这是我们新推出的领先水平、最先进的（SOTA）模型，发布于 **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**。 YOLOv8 旨在快速、准确且易于使用，使其成为广泛的物体检测、图像分割和图像分类任务的极佳选择。 + +请查看 [YOLOv8 文档](https://docs.ultralytics.com)了解详细信息，并开始使用： + +[![PyPI 版本](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![下载量](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics) + +```commandline +pip install ultralytics +``` + +
+ + +
+ +##
文档
+ +有关训练、测试和部署的完整文档见[YOLOv5 文档](https://docs.ultralytics.com/yolov5/)。请参阅下面的快速入门示例。 + +
+安装 + +克隆 repo，并要求在 [**Python>=3.8.0**](https://www.python.org/) 环境中安装 [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) ，且要求 [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/) 。 + +```bash +git clone https://github.com/ultralytics/yolov5 # clone +cd yolov5 +pip install -r requirements.txt # install +``` + +
+ +
+推理 + +使用 YOLOv5 [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) 推理。最新 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 + +```python +import torch + +# Model +model = torch.hub.load("ultralytics/yolov5", "yolov5s") # or yolov5n - yolov5x6, custom + +# Images +img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list + +# Inference +results = model(img) + +# Results +results.print() # or .show(), .save(), .crop(), .pandas(), etc. +``` + +
+ +
+使用 detect.py 推理 + +`detect.py` 在各种来源上运行推理， [模型](https://github.com/ultralytics/yolov5/tree/master/models) 自动从 最新的YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载，并将结果保存到 `runs/detect` 。 + +```bash +python detect.py --weights yolov5s.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/LNwODJXcvt4' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream +``` + +
+ +
+训练 + +下面的命令重现 YOLOv5 在 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) 数据集上的结果。 最新的 [模型](https://github.com/ultralytics/yolov5/tree/master/models) 和 [数据集](https://github.com/ultralytics/yolov5/tree/master/data) +将自动的从 YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) 中下载。 YOLOv5n/s/m/l/x 在 V100 GPU 的训练时间为 1/2/4/6/8 天（ [多GPU](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) 训练速度更快）。 尽可能使用更大的 `--batch-size` ，或通过 `--batch-size -1` 实现 YOLOv5 [自动批处理](https://github.com/ultralytics/yolov5/pull/5092) 。下方显示的 batchsize 适用于 V100-16GB。 + +```bash +python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128 + yolov5s 64 + yolov5m 40 + yolov5l 24 + yolov5x 16 +``` + + + +
+ +
+教程 + +- [训练自定义数据](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data) 🚀 推荐 +- [获得最佳训练结果的技巧](https://docs.ultralytics.com/guides/model-training-tips/) ☘️ +- [多GPU训练](https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training) +- [PyTorch Hub](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) 🌟 新 +- [TFLite，ONNX，CoreML，TensorRT导出](https://docs.ultralytics.com/yolov5/tutorials/model_export) 🚀 +- [NVIDIA Jetson平台部署](https://docs.ultralytics.com/yolov5/tutorials/running_on_jetson_nano) 🌟 新 +- [测试时增强 (TTA)](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation) +- [模型集成](https://docs.ultralytics.com/yolov5/tutorials/model_ensembling) +- [模型剪枝/稀疏](https://docs.ultralytics.com/yolov5/tutorials/model_pruning_and_sparsity) +- [超参数进化](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution) +- [冻结层的迁移学习](https://docs.ultralytics.com/yolov5/tutorials/transfer_learning_with_frozen_layers) +- [架构概述](https://docs.ultralytics.com/yolov5/tutorials/architecture_description) 🌟 新 +- [Roboflow用于数据集、标注和主动学习](https://docs.ultralytics.com/yolov5/tutorials/roboflow_datasets_integration) +- [ClearML日志记录](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) 🌟 新 +- [使用Neural Magic的Deepsparse的YOLOv5](https://docs.ultralytics.com/yolov5/tutorials/neural_magic_pruning_quantization) 🌟 新 +- [Comet日志记录](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration) 🌟 新 + +
+ +##
模块集成
+ +
+ + +
+
+ +
+ + + + + + + + + + + +
+ +| Roboflow | ClearML ⭐ 新 | Comet ⭐ 新 | Neural Magic ⭐ 新 | +| :--------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | +| 将您的自定义数据集进行标注并直接导出到 YOLOv5 以进行训练 [Roboflow](https://roboflow.com/?ref=ultralytics) | 自动跟踪、可视化甚至远程训练 YOLOv5 [ClearML](https://cutt.ly/yolov5-readme-clearml)（开源！） | 永远免费，[Comet](https://bit.ly/yolov5-readme-comet2)可让您保存 YOLOv5 模型、恢复训练以及交互式可视化和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic)，运行 YOLOv5 推理的速度最高可提高6倍 | + +##
Ultralytics HUB
+ +[Ultralytics HUB](https://ultralytics.com/hub) 是我们的⭐**新的**用于可视化数据集、训练 YOLOv5 🚀 模型并以无缝体验部署到现实世界的无代码解决方案。现在开始 **免费** 使用他！ + + + + +##
为什么选择 YOLOv5
+ +YOLOv5 超级容易上手，简单易学。我们优先考虑现实世界的结果。 + +

+
+ YOLOv5-P5 640 图 + +

+
+
+ 图表笔记 + +- **COCO AP val** 表示 mAP@0.5:0.95 指标，在 [COCO val2017](http://cocodataset.org) 数据集的 5000 张图像上测得， 图像包含 256 到 1536 各种推理大小。 +- **显卡推理速度** 为在 [COCO val2017](http://cocodataset.org) 数据集上的平均推理时间，使用 [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100实例，batchsize 为 32 。 +- **EfficientDet** 数据来自 [google/automl](https://github.com/google/automl) ， batchsize 为32。 +- **复现命令** 为 `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` + +
+ +### 预训练模型 + +| 模型 | 尺寸
（像素） | mAPval
50-95 | mAPval
50 | 推理速度
CPU b1
（ms） | 推理速度
V100 b1
（ms） | 速度
V100 b32
（ms） | 参数量
(M) | FLOPs
@640 (B) | +| ---------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | --------------------------------- | ---------------------------------- | ------------------------------- | ------------------ | ---------------------- | +| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** | +| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 | +| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 | +| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 | +| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 | +| | | | | | | | | | +| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 | +| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 | +| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 | +| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 | +| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)
+[TTA] | 1280
1536 | 55.0
**55.8** | 72.7
**72.7** | 3136
- | 26.2
- | 19.4
- | 140.7
- | 209.8
- | + +
+ 笔记 + +- 所有模型都使用默认配置，训练 300 epochs。n和s模型使用 [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) ，其他模型都使用 [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml) 。 +- \*\*mAPval\*\*在单模型单尺度上计算，数据集使用 [COCO val2017](http://cocodataset.org) 。
复现命令 `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` +- **推理速度**在 COCO val 图像总体时间上进行平均得到，测试环境使用[AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/)实例。 NMS 时间 (大约 1 ms/img) 不包括在内。
复现命令 `python val.py --data coco.yaml --img 640 --task speed --batch 1` +- **TTA** [测试时数据增强](https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation) 包括反射和尺度变换。
复现命令 `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` + +
+ +##
实例分割模型 ⭐ 新
+ +我们新的 YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) 实例分割模型是世界上最快和最准确的模型，击败所有当前 [SOTA 基准](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco)。我们使它非常易于训练、验证和部署。更多细节请查看 [发行说明](https://github.com/ultralytics/yolov5/releases/v7.0) 或访问我们的 [YOLOv5 分割 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) 以快速入门。 + +
+ 实例分割模型列表 + +
+ +
+ + +
+ +我们使用 A100 GPU 在 COCO 上以 640 图像大小训练了 300 epochs 得到 YOLOv5 分割模型。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于再现，我们在 Google [Colab Pro](https://colab.research.google.com/signup) 上进行了所有速度测试。 + +| 模型 | 尺寸
（像素） | mAPbox
50-95 | mAPmask
50-95 | 训练时长
300 epochs
A100 GPU（小时） | 推理速度
ONNX CPU
（ms） | 推理速度
TRT A100
（ms） | 参数量
(M) | FLOPs
@640 (B) | +| ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | ----------------------------------------------- | ----------------------------------- | ----------------------------------- | ------------------ | ---------------------- | +| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** | +| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 | +| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 | +| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 | +| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 | + +- 所有模型使用 SGD 优化器训练， 都使用 `lr0=0.01` 和 `weight_decay=5e-5` 参数， 图像大小为 640 。
训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5_v70_official +- **准确性**结果都在 COCO 数据集上，使用单模型单尺度测试得到。
复现命令 `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt` +- **推理速度**是使用 100 张图像推理时间进行平均得到，测试环境使用 [Colab Pro](https://colab.research.google.com/signup) 上 A100 高 RAM 实例。结果仅表示推理速度（NMS 每张图像增加约 1 毫秒）。
复现命令 `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1` +- **模型转换**到 FP32 的 ONNX 和 FP16 的 TensorRT 脚本为 `export.py`.
运行命令 `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half` + +
+ +
+ 分割模型使用示例  Open In Colab + +### 训练 + +YOLOv5分割训练支持自动下载 COCO128-seg 分割数据集，用户仅需在启动指令中包含 `--data coco128-seg.yaml` 参数。 若要手动下载，使用命令 `bash data/scripts/get_coco.sh --train --val --segments`， 在下载完毕后，使用命令 `python train.py --data coco.yaml` 开启训练。 + +```bash +# 单 GPU +python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 + +# 多 GPU， DDP 模式 +python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3 +``` + +### 验证 + +在 COCO 数据集上验证 YOLOv5s-seg mask mAP： + +```bash +bash data/scripts/get_coco.sh --val --segments # 下载 COCO val segments 数据集 (780MB, 5000 images) +python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # 验证 +``` + +### 预测 + +使用预训练的 YOLOv5m-seg.pt 来预测 bus.jpg： + +```bash +python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg +``` + +```python +model = torch.hub.load( + "ultralytics/yolov5", "custom", "yolov5m-seg.pt" +) # 从load from PyTorch Hub 加载模型 (WARNING: 推理暂未支持) +``` + +| ![zidane](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![bus](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) | +| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- | + +### 模型导出 + +将 YOLOv5s-seg 模型导出到 ONNX 和 TensorRT： + +```bash +python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0 +``` + +
+ +##
分类网络 ⭐ 新
+ +YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) 带来对分类模型训练、验证和部署的支持！详情请查看 [发行说明](https://github.com/ultralytics/yolov5/releases/v6.2) 或访问我们的 [YOLOv5 分类 Colab 笔记本](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) 以快速入门。 + +
+ 分类网络模型 + +
+ +我们使用 4xA100 实例在 ImageNet 上训练了 90 个 epochs 得到 YOLOv5-cls 分类模型，我们训练了 ResNet 和 EfficientNet 模型以及相同的默认训练设置以进行比较。我们将所有模型导出到 ONNX FP32 以进行 CPU 速度测试，并导出到 TensorRT FP16 以进行 GPU 速度测试。为了便于重现，我们在 Google 上进行了所有速度测试 [Colab Pro](https://colab.research.google.com/signup) 。 + +| 模型 | 尺寸
（像素） | acc
top1 | acc
top5 | 训练时长
90 epochs
4xA100（小时） | 推理速度
ONNX CPU
（ms） | 推理速度
TensorRT V100
（ms） | 参数
(M) | FLOPs
@640 (B) | +| -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ----------------------------------- | ---------------------------------------- | ---------------- | ---------------------- | +| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** | +| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 | +| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 | +| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 | +| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 | +| | | | | | | | | | +| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 | +| [Resnetzch](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 | +| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 | +| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 | +| | | | | | | | | | +| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 | +| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 | +| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 | +| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 | + +
+ Table Notes (点击以展开) + +- 所有模型都使用 SGD 优化器训练 90 个 epochs，都使用 `lr0=0.001` 和 `weight_decay=5e-5` 参数， 图像大小为 224 ，且都使用默认设置。
训练 log 可以查看 https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2 +- **准确性**都在单模型单尺度上计算，数据集使用 [ImageNet-1k](https://www.image-net.org/index.php) 。
复现命令 `python classify/val.py --data ../datasets/imagenet --img 224` +- **推理速度**是使用 100 个推理图像进行平均得到，测试环境使用谷歌 [Colab Pro](https://colab.research.google.com/signup) V100 高 RAM 实例。
复现命令 `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1` +- **模型导出**到 FP32 的 ONNX 和 FP16 的 TensorRT 使用 `export.py` 。
复现命令 `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224` +
+
+ +
+ 分类训练示例  Open In Colab + +### 训练 + +YOLOv5 分类训练支持自动下载 MNIST、Fashion-MNIST、CIFAR10、CIFAR100、Imagenette、Imagewoof 和 ImageNet 数据集，命令中使用 `--data` 即可。 MNIST 示例 `--data mnist` 。 + +```bash +# 单 GPU +python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128 + +# 多 GPU， DDP 模式 +python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3 +``` + +### 验证 + +在 ImageNet-1k 数据集上验证 YOLOv5m-cls 的准确性： + +```bash +bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) +python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate +``` + +### 预测 + +使用预训练的 YOLOv5s-cls.pt 来预测 bus.jpg： + +```bash +python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg +``` + +```python +model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s-cls.pt") # load from PyTorch Hub +``` + +### 模型导出 + +将一组经过训练的 YOLOv5s-cls、ResNet 和 EfficientNet 模型导出到 ONNX 和 TensorRT： + +```bash +python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224 +``` + +
+ +##
环境
+ +使用下面我们经过验证的环境，在几秒钟内开始使用 YOLOv5 。单击下面的图标了解详细信息。 + +
+ + + + + + + + + + + + + + + + + +
+ +##
贡献
+ +我们喜欢您的意见或建议！我们希望尽可能简单和透明地为 YOLOv5 做出贡献。请看我们的 [投稿指南](https://docs.ultralytics.com/help/contributing/)，并填写 [YOLOv5调查](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) 向我们发送您的体验反馈。感谢我们所有的贡献者！ + + + + + + +##
许可证
+ +Ultralytics 提供两种许可证选项以适应各种使用场景： + +- **AGPL-3.0 许可证**：这个[OSI 批准](https://opensource.org/licenses/)的开源许可证非常适合学生和爱好者，可以推动开放的协作和知识分享。请查看[LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) 文件以了解更多细节。 +- **企业许可证**：专为商业用途设计，该许可证允许将 Ultralytics 的软件和 AI 模型无缝集成到商业产品和服务中，从而绕过 AGPL-3.0 的开源要求。如果您的场景涉及将我们的解决方案嵌入到商业产品中，请通过 [Ultralytics Licensing](https://ultralytics.com/license)与我们联系。 + +##
联系方式
+ +对于 Ultralytics 的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/yolov5/issues)，并加入我们的 [Discord](https://ultralytics.com/discord) 社区进行问题和讨论！ + +
+
+ Ultralytics GitHub + + Ultralytics LinkedIn + + Ultralytics Twitter + + Ultralytics YouTube + + Ultralytics TikTok + + Ultralytics BiliBili + + Ultralytics Discord +
+ +[tta]: https://docs.ultralytics.com/yolov5/tutorials/test_time_augmentation diff --git a/yolov5/benchmarks.py b/yolov5/benchmarks.py new file mode 100644 index 0000000000000000000000000000000000000000..c849eed6f0785d9aa67dabf53e268cab7f50f838 --- /dev/null +++ b/yolov5/benchmarks.py @@ -0,0 +1,178 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +""" +Run YOLOv5 benchmarks on all supported export formats. + +Format | `export.py --include` | Model +--- | --- | --- +PyTorch | - | yolov5s.pt +TorchScript | `torchscript` | yolov5s.torchscript +ONNX | `onnx` | yolov5s.onnx +OpenVINO | `openvino` | yolov5s_openvino_model/ +TensorRT | `engine` | yolov5s.engine +CoreML | `coreml` | yolov5s.mlmodel +TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/ +TensorFlow GraphDef | `pb` | yolov5s.pb +TensorFlow Lite | `tflite` | yolov5s.tflite +TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite +TensorFlow.js | `tfjs` | yolov5s_web_model/ + +Requirements: + $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU + $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU + $ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # TensorRT + +Usage: + $ python benchmarks.py --weights yolov5s.pt --img 640 +""" + +import argparse +import platform +import sys +import time +from pathlib import Path + +import pandas as pd + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +# ROOT = ROOT.relative_to(Path.cwd()) # relative + +import export +from models.experimental import attempt_load +from models.yolo import SegmentationModel +from segment.val import run as val_seg +from utils import notebook_init +from utils.general import LOGGER, check_yaml, file_size, print_args +from utils.torch_utils import select_device +from val import run as val_det + + +def run( + weights=ROOT / "yolov5s.pt", # weights path + imgsz=640, # inference size (pixels) + batch_size=1, # batch size + data=ROOT / "data/coco128.yaml", # dataset.yaml path + device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu + half=False, # use FP16 half-precision inference + test=False, # test exports only + pt_only=False, # test PyTorch only + hard_fail=False, # throw error on benchmark failure +): + """Run YOLOv5 benchmarks on multiple export formats and log results for model performance evaluation.""" + y, t = [], time.time() + device = select_device(device) + model_type = type(attempt_load(weights, fuse=False)) # DetectionModel, SegmentationModel, etc. + for i, (name, f, suffix, cpu, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, CPU, GPU) + try: + assert i not in (9, 10), "inference not supported" # Edge TPU and TF.js are unsupported + assert i != 5 or platform.system() == "Darwin", "inference only supported on macOS>=10.13" # CoreML + if "cpu" in device.type: + assert cpu, "inference not supported on CPU" + if "cuda" in device.type: + assert gpu, "inference not supported on GPU" + + # Export + if f == "-": + w = weights # PyTorch format + else: + w = export.run( + weights=weights, imgsz=[imgsz], include=[f], batch_size=batch_size, device=device, half=half + )[-1] # all others + assert suffix in str(w), "export failed" + + # Validate + if model_type == SegmentationModel: + result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task="speed", half=half) + metric = result[0][7] # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls)) + else: # DetectionModel: + result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task="speed", half=half) + metric = result[0][3] # (p, r, map50, map, *loss(box, obj, cls)) + speed = result[2][1] # times (preprocess, inference, postprocess) + y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)]) # MB, mAP, t_inference + except Exception as e: + if hard_fail: + assert type(e) is AssertionError, f"Benchmark --hard-fail for {name}: {e}" + LOGGER.warning(f"WARNING ⚠️ Benchmark failure for {name}: {e}") + y.append([name, None, None, None]) # mAP, t_inference + if pt_only and i == 0: + break # break after PyTorch + + # Print results + LOGGER.info("\n") + parse_opt() + notebook_init() # print system info + c = ["Format", "Size (MB)", "mAP50-95", "Inference time (ms)"] if map else ["Format", "Export", "", ""] + py = pd.DataFrame(y, columns=c) + LOGGER.info(f"\nBenchmarks complete ({time.time() - t:.2f}s)") + LOGGER.info(str(py if map else py.iloc[:, :2])) + if hard_fail and isinstance(hard_fail, str): + metrics = py["mAP50-95"].array # values to compare to floor + floor = eval(hard_fail) # minimum metric floor to pass, i.e. = 0.29 mAP for YOLOv5n + assert all(x > floor for x in metrics if pd.notna(x)), f"HARD FAIL: mAP50-95 < floor {floor}" + return py + + +def test( + weights=ROOT / "yolov5s.pt", # weights path + imgsz=640, # inference size (pixels) + batch_size=1, # batch size + data=ROOT / "data/coco128.yaml", # dataset.yaml path + device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu + half=False, # use FP16 half-precision inference + test=False, # test exports only + pt_only=False, # test PyTorch only + hard_fail=False, # throw error on benchmark failure +): + """Run YOLOv5 export tests for all supported formats and log the results, including inference speed and mAP.""" + y, t = [], time.time() + device = select_device(device) + for i, (name, f, suffix, gpu) in export.export_formats().iterrows(): # index, (name, file, suffix, gpu-capable) + try: + w = ( + weights + if f == "-" + else export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] + ) # weights + assert suffix in str(w), "export failed" + y.append([name, True]) + except Exception: + y.append([name, False]) # mAP, t_inference + + # Print results + LOGGER.info("\n") + parse_opt() + notebook_init() # print system info + py = pd.DataFrame(y, columns=["Format", "Export"]) + LOGGER.info(f"\nExports complete ({time.time() - t:.2f}s)") + LOGGER.info(str(py)) + return py + + +def parse_opt(): + """Parses command-line arguments for YOLOv5 model inference configuration.""" + parser = argparse.ArgumentParser() + parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="weights path") + parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)") + parser.add_argument("--batch-size", type=int, default=1, help="batch size") + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") + parser.add_argument("--test", action="store_true", help="test exports only") + parser.add_argument("--pt-only", action="store_true", help="test PyTorch only") + parser.add_argument("--hard-fail", nargs="?", const=True, default=False, help="Exception on error or < min metric") + opt = parser.parse_args() + opt.data = check_yaml(opt.data) # check YAML + print_args(vars(opt)) + return opt + + +def main(opt): + """Executes a test run if `opt.test` is True, otherwise starts training or inference with provided options.""" + test(**vars(opt)) if opt.test else run(**vars(opt)) + + +if __name__ == "__main__": + opt = parse_opt() + main(opt) diff --git a/yolov5/classify/predict.py b/yolov5/classify/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..33140e9b56ca5613e470d5f5cb53f690a637ede0 --- /dev/null +++ b/yolov5/classify/predict.py @@ -0,0 +1,241 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +""" +Run YOLOv5 classification inference on images, videos, directories, globs, YouTube, webcam, streams, etc. + +Usage - sources: + $ python classify/predict.py --weights yolov5s-cls.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/LNwODJXcvt4' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream + +Usage - formats: + $ python classify/predict.py --weights yolov5s-cls.pt # PyTorch + yolov5s-cls.torchscript # TorchScript + yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s-cls_openvino_model # OpenVINO + yolov5s-cls.engine # TensorRT + yolov5s-cls.mlmodel # CoreML (macOS-only) + yolov5s-cls_saved_model # TensorFlow SavedModel + yolov5s-cls.pb # TensorFlow GraphDef + yolov5s-cls.tflite # TensorFlow Lite + yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU + yolov5s-cls_paddle_model # PaddlePaddle +""" + +import argparse +import os +import platform +import sys +from pathlib import Path + +import torch +import torch.nn.functional as F + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from ultralytics.utils.plotting import Annotator + +from models.common import DetectMultiBackend +from utils.augmentations import classify_transforms +from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams +from utils.general import ( + LOGGER, + Profile, + check_file, + check_img_size, + check_imshow, + check_requirements, + colorstr, + cv2, + increment_path, + print_args, + strip_optimizer, +) +from utils.torch_utils import select_device, smart_inference_mode + + +@smart_inference_mode() +def run( + weights=ROOT / "yolov5s-cls.pt", # model.pt path(s) + source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam) + data=ROOT / "data/coco128.yaml", # dataset.yaml path + imgsz=(224, 224), # inference size (height, width) + device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu + view_img=False, # show results + save_txt=False, # save results to *.txt + nosave=False, # do not save images/videos + augment=False, # augmented inference + visualize=False, # visualize features + update=False, # update all models + project=ROOT / "runs/predict-cls", # save results to project/name + name="exp", # save results to project/name + exist_ok=False, # existing project/name ok, do not increment + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + vid_stride=1, # video frame-rate stride +): + """Conducts YOLOv5 classification inference on diverse input sources and saves results.""" + source = str(source) + save_img = not nosave and not source.endswith(".txt") # save inference images + is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) + is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://")) + webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file) + screenshot = source.lower().startswith("screen") + if is_url and is_file: + source = check_file(source) # download + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + device = select_device(device) + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, names, pt = model.stride, model.names, model.pt + imgsz = check_img_size(imgsz, s=stride) # check image size + + # Dataloader + bs = 1 # batch_size + if webcam: + view_img = check_imshow(warn=True) + dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride) + bs = len(dataset) + elif screenshot: + dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) + else: + dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride) + vid_path, vid_writer = [None] * bs, [None] * bs + + # Run inference + model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup + seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device)) + for path, im, im0s, vid_cap, s in dataset: + with dt[0]: + im = torch.Tensor(im).to(model.device) + im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 + if len(im.shape) == 3: + im = im[None] # expand for batch dim + + # Inference + with dt[1]: + results = model(im) + + # Post-process + with dt[2]: + pred = F.softmax(results, dim=1) # probabilities + + # Process predictions + for i, prob in enumerate(pred): # per image + seen += 1 + if webcam: # batch_size >= 1 + p, im0, frame = path[i], im0s[i].copy(), dataset.count + s += f"{i}: " + else: + p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0) + + p = Path(p) # to Path + save_path = str(save_dir / p.name) # im.jpg + txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt + + s += "%gx%g " % im.shape[2:] # print string + annotator = Annotator(im0, example=str(names), pil=True) + + # Print results + top5i = prob.argsort(0, descending=True)[:5].tolist() # top 5 indices + s += f"{', '.join(f'{names[j]} {prob[j]:.2f}' for j in top5i)}, " + + # Write results + text = "\n".join(f"{prob[j]:.2f} {names[j]}" for j in top5i) + if save_img or view_img: # Add bbox to image + annotator.text([32, 32], text, txt_color=(255, 255, 255)) + if save_txt: # Write to file + with open(f"{txt_path}.txt", "a") as f: + f.write(text + "\n") + + # Stream results + im0 = annotator.result() + if view_img: + if platform.system() == "Linux" and p not in windows: + windows.append(p) + cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) + cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) + cv2.imshow(str(p), im0) + cv2.waitKey(1) # 1 millisecond + + # Save results (image with detections) + if save_img: + if dataset.mode == "image": + cv2.imwrite(save_path, im0) + else: # 'video' or 'stream' + if vid_path[i] != save_path: # new video + vid_path[i] = save_path + if isinstance(vid_writer[i], cv2.VideoWriter): + vid_writer[i].release() # release previous video writer + if vid_cap: # video + fps = vid_cap.get(cv2.CAP_PROP_FPS) + w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + else: # stream + fps, w, h = 30, im0.shape[1], im0.shape[0] + save_path = str(Path(save_path).with_suffix(".mp4")) # force *.mp4 suffix on results videos + vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) + vid_writer[i].write(im0) + + # Print time (inference-only) + LOGGER.info(f"{s}{dt[1].dt * 1E3:.1f}ms") + + # Print results + t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image + LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t) + if save_txt or save_img: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else "" + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + if update: + strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) + + +def parse_opt(): + """Parses command line arguments for YOLOv5 inference settings including model, source, device, and image size.""" + parser = argparse.ArgumentParser() + parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-cls.pt", help="model path(s)") + parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)") + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path") + parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[224], help="inference size h,w") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--view-img", action="store_true", help="show results") + parser.add_argument("--save-txt", action="store_true", help="save results to *.txt") + parser.add_argument("--nosave", action="store_true", help="do not save images/videos") + parser.add_argument("--augment", action="store_true", help="augmented inference") + parser.add_argument("--visualize", action="store_true", help="visualize features") + parser.add_argument("--update", action="store_true", help="update all models") + parser.add_argument("--project", default=ROOT / "runs/predict-cls", help="save results to project/name") + parser.add_argument("--name", default="exp", help="save results to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") + parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") + parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride") + opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand + print_args(vars(opt)) + return opt + + +def main(opt): + """Executes YOLOv5 model inference with options for ONNX DNN and video frame-rate stride adjustments.""" + check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) + run(**vars(opt)) + + +if __name__ == "__main__": + opt = parse_opt() + main(opt) diff --git a/yolov5/classify/train.py b/yolov5/classify/train.py new file mode 100644 index 0000000000000000000000000000000000000000..8ff9d1582d2acc54fc9791c4820290a1681e9ec1 --- /dev/null +++ b/yolov5/classify/train.py @@ -0,0 +1,382 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +""" +Train a YOLOv5 classifier model on a classification dataset. + +Usage - Single-GPU training: + $ python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224 + +Usage - Multi-GPU DDP training: + $ python -m torch.distributed.run --nproc_per_node 4 --master_port 2022 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3 + +Datasets: --data mnist, fashion-mnist, cifar10, cifar100, imagenette, imagewoof, imagenet, or 'path/to/data' +YOLOv5-cls models: --model yolov5n-cls.pt, yolov5s-cls.pt, yolov5m-cls.pt, yolov5l-cls.pt, yolov5x-cls.pt +Torchvision models: --model resnet50, efficientnet_b0, etc. See https://pytorch.org/vision/stable/models.html +""" + +import argparse +import os +import subprocess +import sys +import time +from copy import deepcopy +from datetime import datetime +from pathlib import Path + +import torch +import torch.distributed as dist +import torch.hub as hub +import torch.optim.lr_scheduler as lr_scheduler +import torchvision +from torch.cuda import amp +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from classify import val as validate +from models.experimental import attempt_load +from models.yolo import ClassificationModel, DetectionModel +from utils.dataloaders import create_classification_dataloader +from utils.general import ( + DATASETS_DIR, + LOGGER, + TQDM_BAR_FORMAT, + WorkingDirectory, + check_git_info, + check_git_status, + check_requirements, + colorstr, + download, + increment_path, + init_seeds, + print_args, + yaml_save, +) +from utils.loggers import GenericLogger +from utils.plots import imshow_cls +from utils.torch_utils import ( + ModelEMA, + de_parallel, + model_info, + reshape_classifier_output, + select_device, + smart_DDP, + smart_optimizer, + smartCrossEntropyLoss, + torch_distributed_zero_first, +) + +LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv("RANK", -1)) +WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) +GIT_INFO = check_git_info() + + +def train(opt, device): + """Trains a YOLOv5 model, managing datasets, model optimization, logging, and saving checkpoints.""" + init_seeds(opt.seed + 1 + RANK, deterministic=True) + save_dir, data, bs, epochs, nw, imgsz, pretrained = ( + opt.save_dir, + Path(opt.data), + opt.batch_size, + opt.epochs, + min(os.cpu_count() - 1, opt.workers), + opt.imgsz, + str(opt.pretrained).lower() == "true", + ) + cuda = device.type != "cpu" + + # Directories + wdir = save_dir / "weights" + wdir.mkdir(parents=True, exist_ok=True) # make dir + last, best = wdir / "last.pt", wdir / "best.pt" + + # Save run settings + yaml_save(save_dir / "opt.yaml", vars(opt)) + + # Logger + logger = GenericLogger(opt=opt, console_logger=LOGGER) if RANK in {-1, 0} else None + + # Download Dataset + with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT): + data_dir = data if data.is_dir() else (DATASETS_DIR / data) + if not data_dir.is_dir(): + LOGGER.info(f"\nDataset not found ⚠️, missing path {data_dir}, attempting download...") + t = time.time() + if str(data) == "imagenet": + subprocess.run(["bash", str(ROOT / "data/scripts/get_imagenet.sh")], shell=True, check=True) + else: + url = f"https://github.com/ultralytics/yolov5/releases/download/v1.0/{data}.zip" + download(url, dir=data_dir.parent) + s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n" + LOGGER.info(s) + + # Dataloaders + nc = len([x for x in (data_dir / "train").glob("*") if x.is_dir()]) # number of classes + trainloader = create_classification_dataloader( + path=data_dir / "train", + imgsz=imgsz, + batch_size=bs // WORLD_SIZE, + augment=True, + cache=opt.cache, + rank=LOCAL_RANK, + workers=nw, + ) + + test_dir = data_dir / "test" if (data_dir / "test").exists() else data_dir / "val" # data/test or data/val + if RANK in {-1, 0}: + testloader = create_classification_dataloader( + path=test_dir, + imgsz=imgsz, + batch_size=bs // WORLD_SIZE * 2, + augment=False, + cache=opt.cache, + rank=-1, + workers=nw, + ) + + # Model + with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(ROOT): + if Path(opt.model).is_file() or opt.model.endswith(".pt"): + model = attempt_load(opt.model, device="cpu", fuse=False) + elif opt.model in torchvision.models.__dict__: # TorchVision models i.e. resnet50, efficientnet_b0 + model = torchvision.models.__dict__[opt.model](weights="IMAGENET1K_V1" if pretrained else None) + else: + m = hub.list("ultralytics/yolov5") # + hub.list('pytorch/vision') # models + raise ModuleNotFoundError(f"--model {opt.model} not found. Available models are: \n" + "\n".join(m)) + if isinstance(model, DetectionModel): + LOGGER.warning("WARNING ⚠️ pass YOLOv5 classifier model with '-cls' suffix, i.e. '--model yolov5s-cls.pt'") + model = ClassificationModel(model=model, nc=nc, cutoff=opt.cutoff or 10) # convert to classification model + reshape_classifier_output(model, nc) # update class count + for m in model.modules(): + if not pretrained and hasattr(m, "reset_parameters"): + m.reset_parameters() + if isinstance(m, torch.nn.Dropout) and opt.dropout is not None: + m.p = opt.dropout # set dropout + for p in model.parameters(): + p.requires_grad = True # for training + model = model.to(device) + + # Info + if RANK in {-1, 0}: + model.names = trainloader.dataset.classes # attach class names + model.transforms = testloader.dataset.torch_transforms # attach inference transforms + model_info(model) + if opt.verbose: + LOGGER.info(model) + images, labels = next(iter(trainloader)) + file = imshow_cls(images[:25], labels[:25], names=model.names, f=save_dir / "train_images.jpg") + logger.log_images(file, name="Train Examples") + logger.log_graph(model, imgsz) # log model + + # Optimizer + optimizer = smart_optimizer(model, opt.optimizer, opt.lr0, momentum=0.9, decay=opt.decay) + + # Scheduler + lrf = 0.01 # final lr (fraction of lr0) + + # lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - lrf) + lrf # cosine + def lf(x): + """Linear learning rate scheduler function, scaling learning rate from initial value to `lrf` over `epochs`.""" + return (1 - x / epochs) * (1 - lrf) + lrf # linear + + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) + # scheduler = lr_scheduler.OneCycleLR(optimizer, max_lr=lr0, total_steps=epochs, pct_start=0.1, + # final_div_factor=1 / 25 / lrf) + + # EMA + ema = ModelEMA(model) if RANK in {-1, 0} else None + + # DDP mode + if cuda and RANK != -1: + model = smart_DDP(model) + + # Train + t0 = time.time() + criterion = smartCrossEntropyLoss(label_smoothing=opt.label_smoothing) # loss function + best_fitness = 0.0 + scaler = amp.GradScaler(enabled=cuda) + val = test_dir.stem # 'val' or 'test' + LOGGER.info( + f'Image sizes {imgsz} train, {imgsz} test\n' + f'Using {nw * WORLD_SIZE} dataloader workers\n' + f"Logging results to {colorstr('bold', save_dir)}\n" + f'Starting {opt.model} training on {data} dataset with {nc} classes for {epochs} epochs...\n\n' + f"{'Epoch':>10}{'GPU_mem':>10}{'train_loss':>12}{f'{val}_loss':>12}{'top1_acc':>12}{'top5_acc':>12}" + ) + for epoch in range(epochs): # loop over the dataset multiple times + tloss, vloss, fitness = 0.0, 0.0, 0.0 # train loss, val loss, fitness + model.train() + if RANK != -1: + trainloader.sampler.set_epoch(epoch) + pbar = enumerate(trainloader) + if RANK in {-1, 0}: + pbar = tqdm(enumerate(trainloader), total=len(trainloader), bar_format=TQDM_BAR_FORMAT) + for i, (images, labels) in pbar: # progress bar + images, labels = images.to(device, non_blocking=True), labels.to(device) + + # Forward + with amp.autocast(enabled=cuda): # stability issues when enabled + loss = criterion(model(images), labels) + + # Backward + scaler.scale(loss).backward() + + # Optimize + scaler.unscale_(optimizer) # unscale gradients + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients + scaler.step(optimizer) + scaler.update() + optimizer.zero_grad() + if ema: + ema.update(model) + + if RANK in {-1, 0}: + # Print + tloss = (tloss * i + loss.item()) / (i + 1) # update mean losses + mem = "%.3gG" % (torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0) # (GB) + pbar.desc = f"{f'{epoch + 1}/{epochs}':>10}{mem:>10}{tloss:>12.3g}" + " " * 36 + + # Test + if i == len(pbar) - 1: # last batch + top1, top5, vloss = validate.run( + model=ema.ema, dataloader=testloader, criterion=criterion, pbar=pbar + ) # test accuracy, loss + fitness = top1 # define fitness as top1 accuracy + + # Scheduler + scheduler.step() + + # Log metrics + if RANK in {-1, 0}: + # Best fitness + if fitness > best_fitness: + best_fitness = fitness + + # Log + metrics = { + "train/loss": tloss, + f"{val}/loss": vloss, + "metrics/accuracy_top1": top1, + "metrics/accuracy_top5": top5, + "lr/0": optimizer.param_groups[0]["lr"], + } # learning rate + logger.log_metrics(metrics, epoch) + + # Save model + final_epoch = epoch + 1 == epochs + if (not opt.nosave) or final_epoch: + ckpt = { + "epoch": epoch, + "best_fitness": best_fitness, + "model": deepcopy(ema.ema).half(), # deepcopy(de_parallel(model)).half(), + "ema": None, # deepcopy(ema.ema).half(), + "updates": ema.updates, + "optimizer": None, # optimizer.state_dict(), + "opt": vars(opt), + "git": GIT_INFO, # {remote, branch, commit} if a git repo + "date": datetime.now().isoformat(), + } + + # Save last, best and delete + torch.save(ckpt, last) + if best_fitness == fitness: + torch.save(ckpt, best) + del ckpt + + # Train complete + if RANK in {-1, 0} and final_epoch: + LOGGER.info( + f'\nTraining complete ({(time.time() - t0) / 3600:.3f} hours)' + f"\nResults saved to {colorstr('bold', save_dir)}" + f'\nPredict: python classify/predict.py --weights {best} --source im.jpg' + f'\nValidate: python classify/val.py --weights {best} --data {data_dir}' + f'\nExport: python export.py --weights {best} --include onnx' + f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{best}')" + f'\nVisualize: https://netron.app\n' + ) + + # Plot examples + images, labels = (x[:25] for x in next(iter(testloader))) # first 25 images and labels + pred = torch.max(ema.ema(images.to(device)), 1)[1] + file = imshow_cls(images, labels, pred, de_parallel(model).names, verbose=False, f=save_dir / "test_images.jpg") + + # Log results + meta = {"epochs": epochs, "top1_acc": best_fitness, "date": datetime.now().isoformat()} + logger.log_images(file, name="Test Examples (true-predicted)", epoch=epoch) + logger.log_model(best, epochs, metadata=meta) + + +def parse_opt(known=False): + """Parses command line arguments for YOLOv5 training including model path, dataset, epochs, and more, returning + parsed arguments. + """ + parser = argparse.ArgumentParser() + parser.add_argument("--model", type=str, default="yolov5s-cls.pt", help="initial weights path") + parser.add_argument("--data", type=str, default="imagenette160", help="cifar10, cifar100, mnist, imagenet, ...") + parser.add_argument("--epochs", type=int, default=10, help="total training epochs") + parser.add_argument("--batch-size", type=int, default=64, help="total batch size for all GPUs") + parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=224, help="train, val image size (pixels)") + parser.add_argument("--nosave", action="store_true", help="only save final checkpoint") + parser.add_argument("--cache", type=str, nargs="?", const="ram", help='--cache images in "ram" (default) or "disk"') + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") + parser.add_argument("--project", default=ROOT / "runs/train-cls", help="save to project/name") + parser.add_argument("--name", default="exp", help="save to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--pretrained", nargs="?", const=True, default=True, help="start from i.e. --pretrained False") + parser.add_argument("--optimizer", choices=["SGD", "Adam", "AdamW", "RMSProp"], default="Adam", help="optimizer") + parser.add_argument("--lr0", type=float, default=0.001, help="initial learning rate") + parser.add_argument("--decay", type=float, default=5e-5, help="weight decay") + parser.add_argument("--label-smoothing", type=float, default=0.1, help="Label smoothing epsilon") + parser.add_argument("--cutoff", type=int, default=None, help="Model layer cutoff index for Classify() head") + parser.add_argument("--dropout", type=float, default=None, help="Dropout (fraction)") + parser.add_argument("--verbose", action="store_true", help="Verbose mode") + parser.add_argument("--seed", type=int, default=0, help="Global training seed") + parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify") + return parser.parse_known_args()[0] if known else parser.parse_args() + + +def main(opt): + """Executes YOLOv5 training with given options, handling device setup and DDP mode; includes pre-training checks.""" + if RANK in {-1, 0}: + print_args(vars(opt)) + check_git_status() + check_requirements(ROOT / "requirements.txt") + + # DDP mode + device = select_device(opt.device, batch_size=opt.batch_size) + if LOCAL_RANK != -1: + assert opt.batch_size != -1, "AutoBatch is coming soon for classification, please pass a valid --batch-size" + assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE" + assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command" + torch.cuda.set_device(LOCAL_RANK) + device = torch.device("cuda", LOCAL_RANK) + dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo") + + # Parameters + opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run + + # Train + train(opt, device) + + +def run(**kwargs): + """ + Executes YOLOv5 model training or inference with specified parameters, returning updated options. + + Example: from yolov5 import classify; classify.train.run(data=mnist, imgsz=320, model='yolov5m') + """ + opt = parse_opt(True) + for k, v in kwargs.items(): + setattr(opt, k, v) + main(opt) + return opt + + +if __name__ == "__main__": + opt = parse_opt() + main(opt) diff --git a/yolov5/classify/tutorial.ipynb b/yolov5/classify/tutorial.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..f85715ca844ea09e0b12df117c69034635d2060f --- /dev/null +++ b/yolov5/classify/tutorial.ipynb @@ -0,0 +1,1481 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "t6MPjfT5NrKQ" + }, + "source": [ + "
\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "
\n", + " \"Run\n", + " \"Open\n", + " \"Open\n", + "
\n", + "\n", + "This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
See GitHub for community support or contact us for professional support.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7mGmQbAO5pQb" + }, + "source": [ + "# Setup\n", + "\n", + "Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wbvMlHd_QwMG", + "outputId": "0806e375-610d-4ec0-c867-763dbb518279" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\n" + ] + } + ], + "source": [ + "!git clone https://github.com/ultralytics/yolov5 # clone\n", + "%cd yolov5\n", + "%pip install -qr requirements.txt # install\n", + "\n", + "import torch\n", + "import utils\n", + "display = utils.notebook_init() # checks" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4JnkELT0cIJg" + }, + "source": [ + "# 1. Predict\n", + "\n", + "`classify/predict.py` runs YOLOv5 Classification inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/predict-cls`. Example inference sources are:\n", + "\n", + "```shell\n", + "python classify/predict.py --source 0 # webcam\n", + " img.jpg # image \n", + " vid.mp4 # video\n", + " screen # screenshot\n", + " path/ # directory\n", + " 'path/*.jpg' # glob\n", + " 'https://youtu.be/LNwODJXcvt4' # YouTube\n", + " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zR9ZbuQCH7FX", + "outputId": "50504ef7-aa3e-4281-a4e3-d0c7df3c0ffe" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mclassify/predict: \u001b[0mweights=['yolov5s-cls.pt'], source=data/images, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=False, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False, vid_stride=1\n", + "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt to yolov5s-cls.pt...\n", + "100% 10.5M/10.5M [00:00<00:00, 12.3MB/s]\n", + "\n", + "Fusing layers... \n", + "Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\n", + "image 1/2 /content/yolov5/data/images/bus.jpg: 224x224 minibus 0.39, police van 0.24, amphibious vehicle 0.05, recreational vehicle 0.04, trolleybus 0.03, 3.9ms\n", + "image 2/2 /content/yolov5/data/images/zidane.jpg: 224x224 suit 0.38, bow tie 0.19, bridegroom 0.18, rugby ball 0.04, stage 0.02, 4.6ms\n", + "Speed: 0.3ms pre-process, 4.3ms inference, 1.5ms NMS per image at shape (1, 3, 224, 224)\n", + "Results saved to \u001b[1mruns/predict-cls/exp\u001b[0m\n" + ] + } + ], + "source": [ + "!python classify/predict.py --weights yolov5s-cls.pt --img 224 --source data/images\n", + "# display.Image(filename='runs/predict-cls/exp/zidane.jpg', width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hkAzDWJ7cWTr" + }, + "source": [ + "        \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0eq1SMWl6Sfn" + }, + "source": [ + "# 2. Validate\n", + "Validate a model's accuracy on the [Imagenet](https://image-net.org/) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WQPtK1QYVaD_", + "outputId": "20fc0630-141e-4a90-ea06-342cbd7ce496" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2022-11-22 19:53:40-- https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar\n", + "Resolving image-net.org (image-net.org)... 171.64.68.16\n", + "Connecting to image-net.org (image-net.org)|171.64.68.16|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 6744924160 (6.3G) [application/x-tar]\n", + "Saving to: ‘ILSVRC2012_img_val.tar’\n", + "\n", + "ILSVRC2012_img_val. 100%[===================>] 6.28G 16.1MB/s in 10m 52s \n", + "\n", + "2022-11-22 20:04:32 (9.87 MB/s) - ‘ILSVRC2012_img_val.tar’ saved [6744924160/6744924160]\n", + "\n" + ] + } + ], + "source": [ + "# Download Imagenet val (6.3G, 50000 images)\n", + "!bash data/scripts/get_imagenet.sh --val" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X58w8JLpMnjH", + "outputId": "41843132-98e2-4c25-d474-4cd7b246fb8e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mclassify/val: \u001b[0mdata=../datasets/imagenet, weights=['yolov5s-cls.pt'], batch_size=128, imgsz=224, device=, workers=8, verbose=True, project=runs/val-cls, name=exp, exist_ok=False, half=True, dnn=False\n", + "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "Fusing layers... \n", + "Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\n", + "validating: 100% 391/391 [04:57<00:00, 1.31it/s]\n", + " Class Images top1_acc top5_acc\n", + " all 50000 0.715 0.902\n", + " tench 50 0.94 0.98\n", + " goldfish 50 0.88 0.92\n", + " great white shark 50 0.78 0.96\n", + " tiger shark 50 0.68 0.96\n", + " hammerhead shark 50 0.82 0.92\n", + " electric ray 50 0.76 0.9\n", + " stingray 50 0.7 0.9\n", + " cock 50 0.78 0.92\n", + " hen 50 0.84 0.96\n", + " ostrich 50 0.98 1\n", + " brambling 50 0.9 0.96\n", + " goldfinch 50 0.92 0.98\n", + " house finch 50 0.88 0.96\n", + " junco 50 0.94 0.98\n", + " indigo bunting 50 0.86 0.88\n", + " American robin 50 0.9 0.96\n", + " bulbul 50 0.84 0.96\n", + " jay 50 0.9 0.96\n", + " magpie 50 0.84 0.96\n", + " chickadee 50 0.9 1\n", + " American dipper 50 0.82 0.92\n", + " kite 50 0.76 0.94\n", + " bald eagle 50 0.92 1\n", + " vulture 50 0.96 1\n", + " great grey owl 50 0.94 0.98\n", + " fire salamander 50 0.96 0.98\n", + " smooth newt 50 0.58 0.94\n", + " newt 50 0.74 0.9\n", + " spotted salamander 50 0.86 0.94\n", + " axolotl 50 0.86 0.96\n", + " American bullfrog 50 0.78 0.92\n", + " tree frog 50 0.84 0.96\n", + " tailed frog 50 0.48 0.8\n", + " loggerhead sea turtle 50 0.68 0.94\n", + " leatherback sea turtle 50 0.5 0.8\n", + " mud turtle 50 0.64 0.84\n", + " terrapin 50 0.52 0.98\n", + " box turtle 50 0.84 0.98\n", + " banded gecko 50 0.7 0.88\n", + " green iguana 50 0.76 0.94\n", + " Carolina anole 50 0.58 0.96\n", + "desert grassland whiptail lizard 50 0.82 0.94\n", + " agama 50 0.74 0.92\n", + " frilled-necked lizard 50 0.84 0.86\n", + " alligator lizard 50 0.58 0.78\n", + " Gila monster 50 0.72 0.8\n", + " European green lizard 50 0.42 0.9\n", + " chameleon 50 0.76 0.84\n", + " Komodo dragon 50 0.86 0.96\n", + " Nile crocodile 50 0.7 0.84\n", + " American alligator 50 0.76 0.96\n", + " triceratops 50 0.9 0.94\n", + " worm snake 50 0.76 0.88\n", + " ring-necked snake 50 0.8 0.92\n", + " eastern hog-nosed snake 50 0.58 0.88\n", + " smooth green snake 50 0.6 0.94\n", + " kingsnake 50 0.82 0.9\n", + " garter snake 50 0.88 0.94\n", + " water snake 50 0.7 0.94\n", + " vine snake 50 0.66 0.76\n", + " night snake 50 0.34 0.82\n", + " boa constrictor 50 0.8 0.96\n", + " African rock python 50 0.48 0.76\n", + " Indian cobra 50 0.82 0.94\n", + " green mamba 50 0.54 0.86\n", + " sea snake 50 0.62 0.9\n", + " Saharan horned viper 50 0.56 0.86\n", + "eastern diamondback rattlesnake 50 0.6 0.86\n", + " sidewinder 50 0.28 0.86\n", + " trilobite 50 0.98 0.98\n", + " harvestman 50 0.86 0.94\n", + " scorpion 50 0.86 0.94\n", + " yellow garden spider 50 0.92 0.96\n", + " barn spider 50 0.38 0.98\n", + " European garden spider 50 0.62 0.98\n", + " southern black widow 50 0.88 0.94\n", + " tarantula 50 0.94 1\n", + " wolf spider 50 0.82 0.92\n", + " tick 50 0.74 0.84\n", + " centipede 50 0.68 0.82\n", + " black grouse 50 0.88 0.98\n", + " ptarmigan 50 0.78 0.94\n", + " ruffed grouse 50 0.88 1\n", + " prairie grouse 50 0.92 1\n", + " peacock 50 0.88 0.9\n", + " quail 50 0.9 0.94\n", + " partridge 50 0.74 0.96\n", + " grey parrot 50 0.9 0.96\n", + " macaw 50 0.88 0.98\n", + "sulphur-crested cockatoo 50 0.86 0.92\n", + " lorikeet 50 0.96 1\n", + " coucal 50 0.82 0.88\n", + " bee eater 50 0.96 0.98\n", + " hornbill 50 0.9 0.96\n", + " hummingbird 50 0.88 0.96\n", + " jacamar 50 0.92 0.94\n", + " toucan 50 0.84 0.94\n", + " duck 50 0.76 0.94\n", + " red-breasted merganser 50 0.86 0.96\n", + " goose 50 0.74 0.96\n", + " black swan 50 0.94 0.98\n", + " tusker 50 0.54 0.92\n", + " echidna 50 0.98 1\n", + " platypus 50 0.72 0.84\n", + " wallaby 50 0.78 0.88\n", + " koala 50 0.84 0.92\n", + " wombat 50 0.78 0.84\n", + " jellyfish 50 0.88 0.96\n", + " sea anemone 50 0.72 0.9\n", + " brain coral 50 0.88 0.96\n", + " flatworm 50 0.8 0.98\n", + " nematode 50 0.86 0.9\n", + " conch 50 0.74 0.88\n", + " snail 50 0.78 0.88\n", + " slug 50 0.74 0.82\n", + " sea slug 50 0.88 0.98\n", + " chiton 50 0.88 0.98\n", + " chambered nautilus 50 0.88 0.92\n", + " Dungeness crab 50 0.78 0.94\n", + " rock crab 50 0.68 0.86\n", + " fiddler crab 50 0.64 0.86\n", + " red king crab 50 0.76 0.96\n", + " American lobster 50 0.78 0.96\n", + " spiny lobster 50 0.74 0.88\n", + " crayfish 50 0.56 0.86\n", + " hermit crab 50 0.78 0.96\n", + " isopod 50 0.66 0.78\n", + " white stork 50 0.88 0.96\n", + " black stork 50 0.84 0.98\n", + " spoonbill 50 0.96 1\n", + " flamingo 50 0.94 1\n", + " little blue heron 50 0.92 0.98\n", + " great egret 50 0.9 0.96\n", + " bittern 50 0.86 0.94\n", + " crane (bird) 50 0.62 0.9\n", + " limpkin 50 0.98 1\n", + " common gallinule 50 0.92 0.96\n", + " American coot 50 0.9 0.98\n", + " bustard 50 0.92 0.96\n", + " ruddy turnstone 50 0.94 1\n", + " dunlin 50 0.86 0.94\n", + " common redshank 50 0.9 0.96\n", + " dowitcher 50 0.84 0.96\n", + " oystercatcher 50 0.86 0.94\n", + " pelican 50 0.92 0.96\n", + " king penguin 50 0.88 0.96\n", + " albatross 50 0.9 1\n", + " grey whale 50 0.84 0.92\n", + " killer whale 50 0.92 1\n", + " dugong 50 0.84 0.96\n", + " sea lion 50 0.82 0.92\n", + " Chihuahua 50 0.66 0.84\n", + " Japanese Chin 50 0.72 0.98\n", + " Maltese 50 0.76 0.94\n", + " Pekingese 50 0.84 0.94\n", + " Shih Tzu 50 0.74 0.96\n", + " King Charles Spaniel 50 0.88 0.98\n", + " Papillon 50 0.86 0.94\n", + " toy terrier 50 0.48 0.94\n", + " Rhodesian Ridgeback 50 0.76 0.98\n", + " Afghan Hound 50 0.84 1\n", + " Basset Hound 50 0.8 0.92\n", + " Beagle 50 0.82 0.96\n", + " Bloodhound 50 0.48 0.72\n", + " Bluetick Coonhound 50 0.86 0.94\n", + " Black and Tan Coonhound 50 0.54 0.8\n", + "Treeing Walker Coonhound 50 0.66 0.98\n", + " English foxhound 50 0.32 0.84\n", + " Redbone Coonhound 50 0.62 0.94\n", + " borzoi 50 0.92 1\n", + " Irish Wolfhound 50 0.48 0.88\n", + " Italian Greyhound 50 0.76 0.98\n", + " Whippet 50 0.74 0.92\n", + " Ibizan Hound 50 0.6 0.86\n", + " Norwegian Elkhound 50 0.88 0.98\n", + " Otterhound 50 0.62 0.9\n", + " Saluki 50 0.72 0.92\n", + " Scottish Deerhound 50 0.86 0.98\n", + " Weimaraner 50 0.88 0.94\n", + "Staffordshire Bull Terrier 50 0.66 0.98\n", + "American Staffordshire Terrier 50 0.64 0.92\n", + " Bedlington Terrier 50 0.9 0.92\n", + " Border Terrier 50 0.86 0.92\n", + " Kerry Blue Terrier 50 0.78 0.98\n", + " Irish Terrier 50 0.7 0.96\n", + " Norfolk Terrier 50 0.68 0.9\n", + " Norwich Terrier 50 0.72 1\n", + " Yorkshire Terrier 50 0.66 0.9\n", + " Wire Fox Terrier 50 0.64 0.98\n", + " Lakeland Terrier 50 0.74 0.92\n", + " Sealyham Terrier 50 0.76 0.9\n", + " Airedale Terrier 50 0.82 0.92\n", + " Cairn Terrier 50 0.76 0.9\n", + " Australian Terrier 50 0.48 0.84\n", + " Dandie Dinmont Terrier 50 0.82 0.92\n", + " Boston Terrier 50 0.92 1\n", + " Miniature Schnauzer 50 0.68 0.9\n", + " Giant Schnauzer 50 0.72 0.98\n", + " Standard Schnauzer 50 0.74 1\n", + " Scottish Terrier 50 0.76 0.96\n", + " Tibetan Terrier 50 0.48 1\n", + "Australian Silky Terrier 50 0.66 0.96\n", + "Soft-coated Wheaten Terrier 50 0.74 0.96\n", + "West Highland White Terrier 50 0.88 0.96\n", + " Lhasa Apso 50 0.68 0.96\n", + " Flat-Coated Retriever 50 0.72 0.94\n", + " Curly-coated Retriever 50 0.82 0.94\n", + " Golden Retriever 50 0.86 0.94\n", + " Labrador Retriever 50 0.82 0.94\n", + "Chesapeake Bay Retriever 50 0.76 0.96\n", + "German Shorthaired Pointer 50 0.8 0.96\n", + " Vizsla 50 0.68 0.96\n", + " English Setter 50 0.7 1\n", + " Irish Setter 50 0.8 0.9\n", + " Gordon Setter 50 0.84 0.92\n", + " Brittany 50 0.84 0.96\n", + " Clumber Spaniel 50 0.92 0.96\n", + "English Springer Spaniel 50 0.88 1\n", + " Welsh Springer Spaniel 50 0.92 1\n", + " Cocker Spaniels 50 0.7 0.94\n", + " Sussex Spaniel 50 0.72 0.92\n", + " Irish Water Spaniel 50 0.88 0.98\n", + " Kuvasz 50 0.66 0.9\n", + " Schipperke 50 0.9 0.98\n", + " Groenendael 50 0.8 0.94\n", + " Malinois 50 0.86 0.98\n", + " Briard 50 0.52 0.8\n", + " Australian Kelpie 50 0.6 0.88\n", + " Komondor 50 0.88 0.94\n", + " Old English Sheepdog 50 0.94 0.98\n", + " Shetland Sheepdog 50 0.74 0.9\n", + " collie 50 0.6 0.96\n", + " Border Collie 50 0.74 0.96\n", + " Bouvier des Flandres 50 0.78 0.94\n", + " Rottweiler 50 0.88 0.96\n", + " German Shepherd Dog 50 0.8 0.98\n", + " Dobermann 50 0.68 0.96\n", + " Miniature Pinscher 50 0.76 0.88\n", + "Greater Swiss Mountain Dog 50 0.68 0.94\n", + " Bernese Mountain Dog 50 0.96 1\n", + " Appenzeller Sennenhund 50 0.22 1\n", + " Entlebucher Sennenhund 50 0.64 0.98\n", + " Boxer 50 0.7 0.92\n", + " Bullmastiff 50 0.78 0.98\n", + " Tibetan Mastiff 50 0.88 0.96\n", + " French Bulldog 50 0.84 0.94\n", + " Great Dane 50 0.54 0.9\n", + " St. Bernard 50 0.92 1\n", + " husky 50 0.46 0.98\n", + " Alaskan Malamute 50 0.76 0.96\n", + " Siberian Husky 50 0.46 0.98\n", + " Dalmatian 50 0.94 0.98\n", + " Affenpinscher 50 0.78 0.9\n", + " Basenji 50 0.92 0.94\n", + " pug 50 0.94 0.98\n", + " Leonberger 50 1 1\n", + " Newfoundland 50 0.78 0.96\n", + " Pyrenean Mountain Dog 50 0.78 0.96\n", + " Samoyed 50 0.96 1\n", + " Pomeranian 50 0.98 1\n", + " Chow Chow 50 0.9 0.96\n", + " Keeshond 50 0.88 0.94\n", + " Griffon Bruxellois 50 0.84 0.98\n", + " Pembroke Welsh Corgi 50 0.82 0.94\n", + " Cardigan Welsh Corgi 50 0.66 0.98\n", + " Toy Poodle 50 0.52 0.88\n", + " Miniature Poodle 50 0.52 0.92\n", + " Standard Poodle 50 0.8 1\n", + " Mexican hairless dog 50 0.88 0.98\n", + " grey wolf 50 0.82 0.92\n", + " Alaskan tundra wolf 50 0.78 0.98\n", + " red wolf 50 0.48 0.9\n", + " coyote 50 0.64 0.86\n", + " dingo 50 0.76 0.88\n", + " dhole 50 0.9 0.98\n", + " African wild dog 50 0.98 1\n", + " hyena 50 0.88 0.96\n", + " red fox 50 0.54 0.92\n", + " kit fox 50 0.72 0.98\n", + " Arctic fox 50 0.94 1\n", + " grey fox 50 0.7 0.94\n", + " tabby cat 50 0.54 0.92\n", + " tiger cat 50 0.22 0.94\n", + " Persian cat 50 0.9 0.98\n", + " Siamese cat 50 0.96 1\n", + " Egyptian Mau 50 0.54 0.8\n", + " cougar 50 0.9 1\n", + " lynx 50 0.72 0.88\n", + " leopard 50 0.78 0.98\n", + " snow leopard 50 0.9 0.98\n", + " jaguar 50 0.7 0.94\n", + " lion 50 0.9 0.98\n", + " tiger 50 0.92 0.98\n", + " cheetah 50 0.94 0.98\n", + " brown bear 50 0.94 0.98\n", + " American black bear 50 0.8 1\n", + " polar bear 50 0.84 0.96\n", + " sloth bear 50 0.72 0.92\n", + " mongoose 50 0.7 0.92\n", + " meerkat 50 0.82 0.92\n", + " tiger beetle 50 0.92 0.94\n", + " ladybug 50 0.86 0.94\n", + " ground beetle 50 0.64 0.94\n", + " longhorn beetle 50 0.62 0.88\n", + " leaf beetle 50 0.64 0.98\n", + " dung beetle 50 0.86 0.98\n", + " rhinoceros beetle 50 0.86 0.94\n", + " weevil 50 0.9 1\n", + " fly 50 0.78 0.94\n", + " bee 50 0.68 0.94\n", + " ant 50 0.68 0.78\n", + " grasshopper 50 0.5 0.92\n", + " cricket 50 0.64 0.92\n", + " stick insect 50 0.64 0.92\n", + " cockroach 50 0.72 0.8\n", + " mantis 50 0.64 0.86\n", + " cicada 50 0.9 0.96\n", + " leafhopper 50 0.88 0.94\n", + " lacewing 50 0.78 0.92\n", + " dragonfly 50 0.82 0.98\n", + " damselfly 50 0.82 1\n", + " red admiral 50 0.94 0.96\n", + " ringlet 50 0.86 0.98\n", + " monarch butterfly 50 0.9 0.92\n", + " small white 50 0.9 1\n", + " sulphur butterfly 50 0.92 1\n", + "gossamer-winged butterfly 50 0.88 1\n", + " starfish 50 0.88 0.92\n", + " sea urchin 50 0.84 0.94\n", + " sea cucumber 50 0.66 0.84\n", + " cottontail rabbit 50 0.72 0.94\n", + " hare 50 0.84 0.96\n", + " Angora rabbit 50 0.94 0.98\n", + " hamster 50 0.96 1\n", + " porcupine 50 0.88 0.98\n", + " fox squirrel 50 0.76 0.94\n", + " marmot 50 0.92 0.96\n", + " beaver 50 0.78 0.94\n", + " guinea pig 50 0.78 0.94\n", + " common sorrel 50 0.96 0.98\n", + " zebra 50 0.94 0.96\n", + " pig 50 0.5 0.76\n", + " wild boar 50 0.84 0.96\n", + " warthog 50 0.84 0.96\n", + " hippopotamus 50 0.88 0.96\n", + " ox 50 0.48 0.94\n", + " water buffalo 50 0.78 0.94\n", + " bison 50 0.88 0.96\n", + " ram 50 0.58 0.92\n", + " bighorn sheep 50 0.66 1\n", + " Alpine ibex 50 0.92 0.98\n", + " hartebeest 50 0.94 1\n", + " impala 50 0.82 0.96\n", + " gazelle 50 0.7 0.96\n", + " dromedary 50 0.9 1\n", + " llama 50 0.82 0.94\n", + " weasel 50 0.44 0.92\n", + " mink 50 0.78 0.96\n", + " European polecat 50 0.46 0.9\n", + " black-footed ferret 50 0.68 0.96\n", + " otter 50 0.66 0.88\n", + " skunk 50 0.96 0.96\n", + " badger 50 0.86 0.92\n", + " armadillo 50 0.88 0.9\n", + " three-toed sloth 50 0.96 1\n", + " orangutan 50 0.78 0.92\n", + " gorilla 50 0.82 0.94\n", + " chimpanzee 50 0.84 0.94\n", + " gibbon 50 0.76 0.86\n", + " siamang 50 0.68 0.94\n", + " guenon 50 0.8 0.94\n", + " patas monkey 50 0.62 0.82\n", + " baboon 50 0.9 0.98\n", + " macaque 50 0.8 0.86\n", + " langur 50 0.6 0.82\n", + " black-and-white colobus 50 0.86 0.9\n", + " proboscis monkey 50 1 1\n", + " marmoset 50 0.74 0.98\n", + " white-headed capuchin 50 0.72 0.9\n", + " howler monkey 50 0.86 0.94\n", + " titi 50 0.5 0.9\n", + "Geoffroy's spider monkey 50 0.42 0.8\n", + " common squirrel monkey 50 0.76 0.92\n", + " ring-tailed lemur 50 0.72 0.94\n", + " indri 50 0.9 0.96\n", + " Asian elephant 50 0.58 0.92\n", + " African bush elephant 50 0.7 0.98\n", + " red panda 50 0.94 0.94\n", + " giant panda 50 0.94 0.98\n", + " snoek 50 0.74 0.9\n", + " eel 50 0.6 0.84\n", + " coho salmon 50 0.84 0.96\n", + " rock beauty 50 0.88 0.98\n", + " clownfish 50 0.78 0.98\n", + " sturgeon 50 0.68 0.94\n", + " garfish 50 0.62 0.8\n", + " lionfish 50 0.96 0.96\n", + " pufferfish 50 0.88 0.96\n", + " abacus 50 0.74 0.88\n", + " abaya 50 0.84 0.92\n", + " academic gown 50 0.42 0.86\n", + " accordion 50 0.8 0.9\n", + " acoustic guitar 50 0.5 0.76\n", + " aircraft carrier 50 0.8 0.96\n", + " airliner 50 0.92 1\n", + " airship 50 0.76 0.82\n", + " altar 50 0.64 0.98\n", + " ambulance 50 0.88 0.98\n", + " amphibious vehicle 50 0.64 0.94\n", + " analog clock 50 0.52 0.92\n", + " apiary 50 0.82 0.96\n", + " apron 50 0.7 0.84\n", + " waste container 50 0.4 0.8\n", + " assault rifle 50 0.42 0.84\n", + " backpack 50 0.34 0.64\n", + " bakery 50 0.4 0.68\n", + " balance beam 50 0.8 0.98\n", + " balloon 50 0.86 0.96\n", + " ballpoint pen 50 0.52 0.96\n", + " Band-Aid 50 0.7 0.9\n", + " banjo 50 0.84 1\n", + " baluster 50 0.68 0.94\n", + " barbell 50 0.56 0.9\n", + " barber chair 50 0.7 0.92\n", + " barbershop 50 0.54 0.86\n", + " barn 50 0.96 0.96\n", + " barometer 50 0.84 0.98\n", + " barrel 50 0.56 0.88\n", + " wheelbarrow 50 0.66 0.88\n", + " baseball 50 0.74 0.98\n", + " basketball 50 0.88 0.98\n", + " bassinet 50 0.66 0.92\n", + " bassoon 50 0.74 0.98\n", + " swimming cap 50 0.62 0.88\n", + " bath towel 50 0.54 0.78\n", + " bathtub 50 0.4 0.88\n", + " station wagon 50 0.66 0.84\n", + " lighthouse 50 0.78 0.94\n", + " beaker 50 0.52 0.68\n", + " military cap 50 0.84 0.96\n", + " beer bottle 50 0.66 0.88\n", + " beer glass 50 0.6 0.84\n", + " bell-cot 50 0.56 0.96\n", + " bib 50 0.58 0.82\n", + " tandem bicycle 50 0.86 0.96\n", + " bikini 50 0.56 0.88\n", + " ring binder 50 0.64 0.84\n", + " binoculars 50 0.54 0.78\n", + " birdhouse 50 0.86 0.94\n", + " boathouse 50 0.74 0.92\n", + " bobsleigh 50 0.92 0.96\n", + " bolo tie 50 0.8 0.94\n", + " poke bonnet 50 0.64 0.86\n", + " bookcase 50 0.66 0.92\n", + " bookstore 50 0.62 0.88\n", + " bottle cap 50 0.58 0.7\n", + " bow 50 0.72 0.86\n", + " bow tie 50 0.7 0.9\n", + " brass 50 0.92 0.96\n", + " bra 50 0.5 0.7\n", + " breakwater 50 0.62 0.86\n", + " breastplate 50 0.4 0.9\n", + " broom 50 0.6 0.86\n", + " bucket 50 0.66 0.8\n", + " buckle 50 0.5 0.68\n", + " bulletproof vest 50 0.5 0.78\n", + " high-speed train 50 0.94 0.96\n", + " butcher shop 50 0.74 0.94\n", + " taxicab 50 0.64 0.86\n", + " cauldron 50 0.44 0.66\n", + " candle 50 0.48 0.74\n", + " cannon 50 0.88 0.94\n", + " canoe 50 0.94 1\n", + " can opener 50 0.66 0.86\n", + " cardigan 50 0.68 0.8\n", + " car mirror 50 0.94 0.96\n", + " carousel 50 0.94 0.98\n", + " tool kit 50 0.56 0.78\n", + " carton 50 0.42 0.7\n", + " car wheel 50 0.38 0.74\n", + "automated teller machine 50 0.76 0.94\n", + " cassette 50 0.52 0.8\n", + " cassette player 50 0.28 0.9\n", + " castle 50 0.78 0.88\n", + " catamaran 50 0.78 1\n", + " CD player 50 0.52 0.82\n", + " cello 50 0.82 1\n", + " mobile phone 50 0.68 0.86\n", + " chain 50 0.38 0.66\n", + " chain-link fence 50 0.7 0.84\n", + " chain mail 50 0.64 0.9\n", + " chainsaw 50 0.84 0.92\n", + " chest 50 0.68 0.92\n", + " chiffonier 50 0.26 0.64\n", + " chime 50 0.62 0.84\n", + " china cabinet 50 0.82 0.96\n", + " Christmas stocking 50 0.92 0.94\n", + " church 50 0.62 0.9\n", + " movie theater 50 0.58 0.88\n", + " cleaver 50 0.32 0.62\n", + " cliff dwelling 50 0.88 1\n", + " cloak 50 0.32 0.64\n", + " clogs 50 0.58 0.88\n", + " cocktail shaker 50 0.62 0.7\n", + " coffee mug 50 0.44 0.72\n", + " coffeemaker 50 0.64 0.92\n", + " coil 50 0.66 0.84\n", + " combination lock 50 0.64 0.84\n", + " computer keyboard 50 0.7 0.82\n", + " confectionery store 50 0.54 0.86\n", + " container ship 50 0.82 0.98\n", + " convertible 50 0.78 0.98\n", + " corkscrew 50 0.82 0.92\n", + " cornet 50 0.46 0.88\n", + " cowboy boot 50 0.64 0.8\n", + " cowboy hat 50 0.64 0.82\n", + " cradle 50 0.38 0.8\n", + " crane (machine) 50 0.78 0.94\n", + " crash helmet 50 0.92 0.96\n", + " crate 50 0.52 0.82\n", + " infant bed 50 0.74 1\n", + " Crock Pot 50 0.78 0.9\n", + " croquet ball 50 0.9 0.96\n", + " crutch 50 0.46 0.7\n", + " cuirass 50 0.54 0.86\n", + " dam 50 0.74 0.92\n", + " desk 50 0.6 0.86\n", + " desktop computer 50 0.54 0.94\n", + " rotary dial telephone 50 0.88 0.94\n", + " diaper 50 0.68 0.84\n", + " digital clock 50 0.54 0.76\n", + " digital watch 50 0.58 0.86\n", + " dining table 50 0.76 0.9\n", + " dishcloth 50 0.94 1\n", + " dishwasher 50 0.44 0.78\n", + " disc brake 50 0.98 1\n", + " dock 50 0.54 0.94\n", + " dog sled 50 0.84 1\n", + " dome 50 0.72 0.92\n", + " doormat 50 0.56 0.82\n", + " drilling rig 50 0.84 0.96\n", + " drum 50 0.38 0.68\n", + " drumstick 50 0.56 0.72\n", + " dumbbell 50 0.62 0.9\n", + " Dutch oven 50 0.7 0.84\n", + " electric fan 50 0.82 0.86\n", + " electric guitar 50 0.62 0.84\n", + " electric locomotive 50 0.92 0.98\n", + " entertainment center 50 0.9 0.98\n", + " envelope 50 0.44 0.86\n", + " espresso machine 50 0.72 0.94\n", + " face powder 50 0.7 0.92\n", + " feather boa 50 0.7 0.84\n", + " filing cabinet 50 0.88 0.98\n", + " fireboat 50 0.94 0.98\n", + " fire engine 50 0.84 0.9\n", + " fire screen sheet 50 0.62 0.76\n", + " flagpole 50 0.74 0.88\n", + " flute 50 0.36 0.72\n", + " folding chair 50 0.62 0.84\n", + " football helmet 50 0.86 0.94\n", + " forklift 50 0.8 0.92\n", + " fountain 50 0.84 0.94\n", + " fountain pen 50 0.76 0.92\n", + " four-poster bed 50 0.78 0.94\n", + " freight car 50 0.96 1\n", + " French horn 50 0.76 0.92\n", + " frying pan 50 0.36 0.78\n", + " fur coat 50 0.84 0.96\n", + " garbage truck 50 0.9 0.98\n", + " gas mask 50 0.84 0.92\n", + " gas pump 50 0.9 0.98\n", + " goblet 50 0.68 0.82\n", + " go-kart 50 0.9 1\n", + " golf ball 50 0.84 0.9\n", + " golf cart 50 0.78 0.86\n", + " gondola 50 0.98 0.98\n", + " gong 50 0.74 0.92\n", + " gown 50 0.62 0.96\n", + " grand piano 50 0.7 0.96\n", + " greenhouse 50 0.8 0.98\n", + " grille 50 0.72 0.9\n", + " grocery store 50 0.66 0.94\n", + " guillotine 50 0.86 0.92\n", + " barrette 50 0.52 0.66\n", + " hair spray 50 0.5 0.74\n", + " half-track 50 0.78 0.9\n", + " hammer 50 0.56 0.76\n", + " hamper 50 0.64 0.84\n", + " hair dryer 50 0.56 0.74\n", + " hand-held computer 50 0.42 0.86\n", + " handkerchief 50 0.78 0.94\n", + " hard disk drive 50 0.76 0.84\n", + " harmonica 50 0.7 0.88\n", + " harp 50 0.88 0.96\n", + " harvester 50 0.78 1\n", + " hatchet 50 0.54 0.74\n", + " holster 50 0.66 0.84\n", + " home theater 50 0.64 0.94\n", + " honeycomb 50 0.56 0.88\n", + " hook 50 0.3 0.6\n", + " hoop skirt 50 0.64 0.86\n", + " horizontal bar 50 0.68 0.98\n", + " horse-drawn vehicle 50 0.88 0.94\n", + " hourglass 50 0.88 0.96\n", + " iPod 50 0.76 0.94\n", + " clothes iron 50 0.82 0.88\n", + " jack-o'-lantern 50 0.98 0.98\n", + " jeans 50 0.68 0.84\n", + " jeep 50 0.72 0.9\n", + " T-shirt 50 0.72 0.96\n", + " jigsaw puzzle 50 0.84 0.94\n", + " pulled rickshaw 50 0.86 0.94\n", + " joystick 50 0.8 0.9\n", + " kimono 50 0.84 0.96\n", + " knee pad 50 0.62 0.88\n", + " knot 50 0.66 0.8\n", + " lab coat 50 0.8 0.96\n", + " ladle 50 0.36 0.64\n", + " lampshade 50 0.48 0.84\n", + " laptop computer 50 0.26 0.88\n", + " lawn mower 50 0.78 0.96\n", + " lens cap 50 0.46 0.72\n", + " paper knife 50 0.26 0.5\n", + " library 50 0.54 0.9\n", + " lifeboat 50 0.92 0.98\n", + " lighter 50 0.56 0.78\n", + " limousine 50 0.76 0.92\n", + " ocean liner 50 0.88 0.94\n", + " lipstick 50 0.74 0.9\n", + " slip-on shoe 50 0.74 0.92\n", + " lotion 50 0.5 0.86\n", + " speaker 50 0.52 0.68\n", + " loupe 50 0.32 0.52\n", + " sawmill 50 0.72 0.9\n", + " magnetic compass 50 0.52 0.82\n", + " mail bag 50 0.68 0.92\n", + " mailbox 50 0.82 0.92\n", + " tights 50 0.22 0.94\n", + " tank suit 50 0.24 0.9\n", + " manhole cover 50 0.96 0.98\n", + " maraca 50 0.74 0.9\n", + " marimba 50 0.84 0.94\n", + " mask 50 0.44 0.82\n", + " match 50 0.66 0.9\n", + " maypole 50 0.96 1\n", + " maze 50 0.8 0.96\n", + " measuring cup 50 0.54 0.76\n", + " medicine chest 50 0.6 0.84\n", + " megalith 50 0.8 0.92\n", + " microphone 50 0.52 0.7\n", + " microwave oven 50 0.48 0.72\n", + " military uniform 50 0.62 0.84\n", + " milk can 50 0.68 0.82\n", + " minibus 50 0.7 1\n", + " miniskirt 50 0.46 0.76\n", + " minivan 50 0.38 0.8\n", + " missile 50 0.4 0.84\n", + " mitten 50 0.76 0.88\n", + " mixing bowl 50 0.8 0.92\n", + " mobile home 50 0.54 0.78\n", + " Model T 50 0.92 0.96\n", + " modem 50 0.58 0.86\n", + " monastery 50 0.44 0.9\n", + " monitor 50 0.4 0.86\n", + " moped 50 0.56 0.94\n", + " mortar 50 0.68 0.94\n", + " square academic cap 50 0.5 0.84\n", + " mosque 50 0.9 1\n", + " mosquito net 50 0.9 0.98\n", + " scooter 50 0.9 0.98\n", + " mountain bike 50 0.78 0.96\n", + " tent 50 0.88 0.96\n", + " computer mouse 50 0.42 0.82\n", + " mousetrap 50 0.76 0.88\n", + " moving van 50 0.4 0.72\n", + " muzzle 50 0.5 0.72\n", + " nail 50 0.68 0.74\n", + " neck brace 50 0.56 0.68\n", + " necklace 50 0.86 1\n", + " nipple 50 0.7 0.88\n", + " notebook computer 50 0.34 0.84\n", + " obelisk 50 0.8 0.92\n", + " oboe 50 0.6 0.84\n", + " ocarina 50 0.8 0.86\n", + " odometer 50 0.96 1\n", + " oil filter 50 0.58 0.82\n", + " organ 50 0.82 0.9\n", + " oscilloscope 50 0.9 0.96\n", + " overskirt 50 0.2 0.7\n", + " bullock cart 50 0.7 0.94\n", + " oxygen mask 50 0.46 0.84\n", + " packet 50 0.5 0.78\n", + " paddle 50 0.56 0.94\n", + " paddle wheel 50 0.86 0.96\n", + " padlock 50 0.74 0.78\n", + " paintbrush 50 0.62 0.8\n", + " pajamas 50 0.56 0.92\n", + " palace 50 0.64 0.96\n", + " pan flute 50 0.84 0.86\n", + " paper towel 50 0.66 0.84\n", + " parachute 50 0.92 0.94\n", + " parallel bars 50 0.62 0.96\n", + " park bench 50 0.74 0.9\n", + " parking meter 50 0.84 0.92\n", + " passenger car 50 0.5 0.82\n", + " patio 50 0.58 0.84\n", + " payphone 50 0.74 0.92\n", + " pedestal 50 0.52 0.9\n", + " pencil case 50 0.64 0.92\n", + " pencil sharpener 50 0.52 0.78\n", + " perfume 50 0.7 0.9\n", + " Petri dish 50 0.6 0.8\n", + " photocopier 50 0.88 0.98\n", + " plectrum 50 0.7 0.84\n", + " Pickelhaube 50 0.72 0.86\n", + " picket fence 50 0.84 0.94\n", + " pickup truck 50 0.64 0.92\n", + " pier 50 0.52 0.82\n", + " piggy bank 50 0.82 0.94\n", + " pill bottle 50 0.76 0.86\n", + " pillow 50 0.76 0.9\n", + " ping-pong ball 50 0.84 0.88\n", + " pinwheel 50 0.76 0.88\n", + " pirate ship 50 0.76 0.94\n", + " pitcher 50 0.46 0.84\n", + " hand plane 50 0.84 0.94\n", + " planetarium 50 0.88 0.98\n", + " plastic bag 50 0.36 0.62\n", + " plate rack 50 0.52 0.78\n", + " plow 50 0.78 0.88\n", + " plunger 50 0.42 0.7\n", + " Polaroid camera 50 0.84 0.92\n", + " pole 50 0.38 0.74\n", + " police van 50 0.76 0.94\n", + " poncho 50 0.58 0.86\n", + " billiard table 50 0.8 0.88\n", + " soda bottle 50 0.56 0.94\n", + " pot 50 0.78 0.92\n", + " potter's wheel 50 0.9 0.94\n", + " power drill 50 0.42 0.72\n", + " prayer rug 50 0.7 0.86\n", + " printer 50 0.54 0.86\n", + " prison 50 0.7 0.9\n", + " projectile 50 0.28 0.9\n", + " projector 50 0.62 0.84\n", + " hockey puck 50 0.92 0.96\n", + " punching bag 50 0.6 0.68\n", + " purse 50 0.42 0.78\n", + " quill 50 0.68 0.84\n", + " quilt 50 0.64 0.9\n", + " race car 50 0.72 0.92\n", + " racket 50 0.72 0.9\n", + " radiator 50 0.66 0.76\n", + " radio 50 0.64 0.92\n", + " radio telescope 50 0.9 0.96\n", + " rain barrel 50 0.8 0.98\n", + " recreational vehicle 50 0.84 0.94\n", + " reel 50 0.72 0.82\n", + " reflex camera 50 0.72 0.92\n", + " refrigerator 50 0.7 0.9\n", + " remote control 50 0.7 0.88\n", + " restaurant 50 0.5 0.66\n", + " revolver 50 0.82 1\n", + " rifle 50 0.38 0.7\n", + " rocking chair 50 0.62 0.84\n", + " rotisserie 50 0.88 0.92\n", + " eraser 50 0.54 0.76\n", + " rugby ball 50 0.86 0.94\n", + " ruler 50 0.68 0.86\n", + " running shoe 50 0.78 0.94\n", + " safe 50 0.82 0.92\n", + " safety pin 50 0.4 0.62\n", + " salt shaker 50 0.66 0.9\n", + " sandal 50 0.66 0.86\n", + " sarong 50 0.64 0.86\n", + " saxophone 50 0.66 0.88\n", + " scabbard 50 0.76 0.92\n", + " weighing scale 50 0.58 0.78\n", + " school bus 50 0.92 1\n", + " schooner 50 0.84 1\n", + " scoreboard 50 0.9 0.96\n", + " CRT screen 50 0.14 0.7\n", + " screw 50 0.9 0.98\n", + " screwdriver 50 0.3 0.58\n", + " seat belt 50 0.88 0.94\n", + " sewing machine 50 0.76 0.9\n", + " shield 50 0.56 0.82\n", + " shoe store 50 0.78 0.96\n", + " shoji 50 0.8 0.92\n", + " shopping basket 50 0.52 0.88\n", + " shopping cart 50 0.76 0.92\n", + " shovel 50 0.62 0.84\n", + " shower cap 50 0.7 0.84\n", + " shower curtain 50 0.64 0.82\n", + " ski 50 0.74 0.92\n", + " ski mask 50 0.72 0.88\n", + " sleeping bag 50 0.68 0.8\n", + " slide rule 50 0.72 0.88\n", + " sliding door 50 0.44 0.78\n", + " slot machine 50 0.94 0.98\n", + " snorkel 50 0.86 0.98\n", + " snowmobile 50 0.88 1\n", + " snowplow 50 0.84 0.98\n", + " soap dispenser 50 0.56 0.86\n", + " soccer ball 50 0.86 0.96\n", + " sock 50 0.62 0.76\n", + " solar thermal collector 50 0.72 0.96\n", + " sombrero 50 0.6 0.84\n", + " soup bowl 50 0.56 0.94\n", + " space bar 50 0.34 0.88\n", + " space heater 50 0.52 0.74\n", + " space shuttle 50 0.82 0.96\n", + " spatula 50 0.3 0.6\n", + " motorboat 50 0.86 1\n", + " spider web 50 0.7 0.9\n", + " spindle 50 0.86 0.98\n", + " sports car 50 0.6 0.94\n", + " spotlight 50 0.26 0.6\n", + " stage 50 0.68 0.86\n", + " steam locomotive 50 0.94 1\n", + " through arch bridge 50 0.84 0.96\n", + " steel drum 50 0.82 0.9\n", + " stethoscope 50 0.6 0.82\n", + " scarf 50 0.5 0.92\n", + " stone wall 50 0.76 0.9\n", + " stopwatch 50 0.58 0.9\n", + " stove 50 0.46 0.74\n", + " strainer 50 0.64 0.84\n", + " tram 50 0.88 0.96\n", + " stretcher 50 0.6 0.8\n", + " couch 50 0.8 0.96\n", + " stupa 50 0.88 0.88\n", + " submarine 50 0.72 0.92\n", + " suit 50 0.4 0.78\n", + " sundial 50 0.58 0.74\n", + " sunglass 50 0.14 0.58\n", + " sunglasses 50 0.28 0.58\n", + " sunscreen 50 0.32 0.7\n", + " suspension bridge 50 0.6 0.94\n", + " mop 50 0.74 0.92\n", + " sweatshirt 50 0.28 0.66\n", + " swimsuit 50 0.52 0.82\n", + " swing 50 0.76 0.84\n", + " switch 50 0.56 0.76\n", + " syringe 50 0.62 0.82\n", + " table lamp 50 0.6 0.88\n", + " tank 50 0.8 0.96\n", + " tape player 50 0.46 0.76\n", + " teapot 50 0.84 1\n", + " teddy bear 50 0.82 0.94\n", + " television 50 0.6 0.9\n", + " tennis ball 50 0.7 0.94\n", + " thatched roof 50 0.88 0.9\n", + " front curtain 50 0.8 0.92\n", + " thimble 50 0.6 0.8\n", + " threshing machine 50 0.56 0.88\n", + " throne 50 0.72 0.82\n", + " tile roof 50 0.72 0.94\n", + " toaster 50 0.66 0.84\n", + " tobacco shop 50 0.42 0.7\n", + " toilet seat 50 0.62 0.88\n", + " torch 50 0.64 0.84\n", + " totem pole 50 0.92 0.98\n", + " tow truck 50 0.62 0.88\n", + " toy store 50 0.6 0.94\n", + " tractor 50 0.76 0.98\n", + " semi-trailer truck 50 0.78 0.92\n", + " tray 50 0.46 0.64\n", + " trench coat 50 0.54 0.72\n", + " tricycle 50 0.72 0.94\n", + " trimaran 50 0.7 0.98\n", + " tripod 50 0.58 0.86\n", + " triumphal arch 50 0.92 0.98\n", + " trolleybus 50 0.9 1\n", + " trombone 50 0.54 0.88\n", + " tub 50 0.24 0.82\n", + " turnstile 50 0.84 0.94\n", + " typewriter keyboard 50 0.68 0.98\n", + " umbrella 50 0.52 0.7\n", + " unicycle 50 0.74 0.96\n", + " upright piano 50 0.76 0.9\n", + " vacuum cleaner 50 0.62 0.9\n", + " vase 50 0.5 0.78\n", + " vault 50 0.76 0.92\n", + " velvet 50 0.2 0.42\n", + " vending machine 50 0.9 1\n", + " vestment 50 0.54 0.82\n", + " viaduct 50 0.78 0.86\n", + " violin 50 0.68 0.78\n", + " volleyball 50 0.86 1\n", + " waffle iron 50 0.72 0.88\n", + " wall clock 50 0.54 0.88\n", + " wallet 50 0.52 0.9\n", + " wardrobe 50 0.68 0.88\n", + " military aircraft 50 0.9 0.98\n", + " sink 50 0.72 0.96\n", + " washing machine 50 0.78 0.94\n", + " water bottle 50 0.54 0.74\n", + " water jug 50 0.22 0.74\n", + " water tower 50 0.9 0.96\n", + " whiskey jug 50 0.64 0.74\n", + " whistle 50 0.72 0.84\n", + " wig 50 0.84 0.9\n", + " window screen 50 0.68 0.8\n", + " window shade 50 0.52 0.76\n", + " Windsor tie 50 0.22 0.66\n", + " wine bottle 50 0.42 0.82\n", + " wing 50 0.54 0.96\n", + " wok 50 0.46 0.82\n", + " wooden spoon 50 0.58 0.8\n", + " wool 50 0.32 0.82\n", + " split-rail fence 50 0.74 0.9\n", + " shipwreck 50 0.84 0.96\n", + " yawl 50 0.78 0.96\n", + " yurt 50 0.84 1\n", + " website 50 0.98 1\n", + " comic book 50 0.62 0.9\n", + " crossword 50 0.84 0.88\n", + " traffic sign 50 0.78 0.9\n", + " traffic light 50 0.8 0.94\n", + " dust jacket 50 0.72 0.94\n", + " menu 50 0.82 0.96\n", + " plate 50 0.44 0.88\n", + " guacamole 50 0.8 0.92\n", + " consomme 50 0.54 0.88\n", + " hot pot 50 0.86 0.98\n", + " trifle 50 0.92 0.98\n", + " ice cream 50 0.68 0.94\n", + " ice pop 50 0.62 0.84\n", + " baguette 50 0.62 0.88\n", + " bagel 50 0.64 0.92\n", + " pretzel 50 0.72 0.88\n", + " cheeseburger 50 0.9 1\n", + " hot dog 50 0.74 0.94\n", + " mashed potato 50 0.74 0.9\n", + " cabbage 50 0.84 0.96\n", + " broccoli 50 0.9 0.96\n", + " cauliflower 50 0.82 1\n", + " zucchini 50 0.74 0.9\n", + " spaghetti squash 50 0.8 0.96\n", + " acorn squash 50 0.82 0.96\n", + " butternut squash 50 0.7 0.94\n", + " cucumber 50 0.6 0.96\n", + " artichoke 50 0.84 0.94\n", + " bell pepper 50 0.84 0.98\n", + " cardoon 50 0.88 0.94\n", + " mushroom 50 0.38 0.92\n", + " Granny Smith 50 0.9 0.96\n", + " strawberry 50 0.6 0.88\n", + " orange 50 0.7 0.92\n", + " lemon 50 0.78 0.98\n", + " fig 50 0.82 0.96\n", + " pineapple 50 0.86 0.96\n", + " banana 50 0.84 0.96\n", + " jackfruit 50 0.9 0.98\n", + " custard apple 50 0.86 0.96\n", + " pomegranate 50 0.82 0.98\n", + " hay 50 0.8 0.92\n", + " carbonara 50 0.88 0.94\n", + " chocolate syrup 50 0.46 0.84\n", + " dough 50 0.4 0.6\n", + " meatloaf 50 0.58 0.84\n", + " pizza 50 0.84 0.96\n", + " pot pie 50 0.68 0.9\n", + " burrito 50 0.8 0.98\n", + " red wine 50 0.54 0.82\n", + " espresso 50 0.64 0.88\n", + " cup 50 0.38 0.7\n", + " eggnog 50 0.38 0.7\n", + " alp 50 0.54 0.88\n", + " bubble 50 0.8 0.96\n", + " cliff 50 0.64 1\n", + " coral reef 50 0.72 0.96\n", + " geyser 50 0.94 1\n", + " lakeshore 50 0.54 0.88\n", + " promontory 50 0.58 0.94\n", + " shoal 50 0.6 0.96\n", + " seashore 50 0.44 0.78\n", + " valley 50 0.72 0.94\n", + " volcano 50 0.78 0.96\n", + " baseball player 50 0.72 0.94\n", + " bridegroom 50 0.72 0.88\n", + " scuba diver 50 0.8 1\n", + " rapeseed 50 0.94 0.98\n", + " daisy 50 0.96 0.98\n", + " yellow lady's slipper 50 1 1\n", + " corn 50 0.4 0.88\n", + " acorn 50 0.92 0.98\n", + " rose hip 50 0.92 0.98\n", + " horse chestnut seed 50 0.94 0.98\n", + " coral fungus 50 0.96 0.96\n", + " agaric 50 0.82 0.94\n", + " gyromitra 50 0.98 1\n", + " stinkhorn mushroom 50 0.8 0.94\n", + " earth star 50 0.98 1\n", + " hen-of-the-woods 50 0.8 0.96\n", + " bolete 50 0.74 0.94\n", + " ear 50 0.48 0.94\n", + " toilet paper 50 0.36 0.68\n", + "Speed: 0.1ms pre-process, 0.3ms inference, 0.0ms post-process per image at shape (1, 3, 224, 224)\n", + "Results saved to \u001b[1mruns/val-cls/exp\u001b[0m\n" + ] + } + ], + "source": [ + "# Validate YOLOv5s on Imagenet val\n", + "!python classify/val.py --weights yolov5s-cls.pt --data ../datasets/imagenet --img 224 --half" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZY2VXXXu74w5" + }, + "source": [ + "# 3. Train\n", + "\n", + "

\n", + "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n", + "

\n", + "\n", + "Train a YOLOv5s Classification model on the [Imagenette](https://image-net.org/) dataset with `--data imagenet`, starting from pretrained `--pretrained yolov5s-cls.pt`.\n", + "\n", + "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n", + "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n", + "- **Training Results** are saved to `runs/train-cls/` with incrementing run directories, i.e. `runs/train-cls/exp2`, `runs/train-cls/exp3` etc.\n", + "

\n", + "\n", + "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n", + "\n", + "## Train on Custom Data with Roboflow 🌟 NEW\n", + "\n", + "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n", + "\n", + "- Custom Training Example: [https://blog.roboflow.com/train-yolov5-classification-custom-data/](https://blog.roboflow.com/train-yolov5-classification-custom-data/?ref=ultralytics)\n", + "- Custom Training Notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1KZiKUAjtARHAfZCXbJRv14-pOnIsBLPV?usp=sharing)\n", + "
\n", + "\n", + "

Label images lightning fast (including with model-assisted labeling)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "i3oKtE4g-aNn" + }, + "outputs": [], + "source": [ + "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n", + "logger = 'Comet' #@param ['Comet', 'ClearML', 'TensorBoard']\n", + "\n", + "if logger == 'Comet':\n", + " %pip install -q comet_ml\n", + " import comet_ml; comet_ml.init()\n", + "elif logger == 'ClearML':\n", + " %pip install -q clearml\n", + " import clearml; clearml.browser_login()\n", + "elif logger == 'TensorBoard':\n", + " %load_ext tensorboard\n", + " %tensorboard --logdir runs/train" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1NcFxRcFdJ_O", + "outputId": "77c8d487-16db-4073-b3ea-06cabf2e7766" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mclassify/train: \u001b[0mmodel=yolov5s-cls.pt, data=imagenette160, epochs=5, batch_size=64, imgsz=224, nosave=False, cache=ram, device=, workers=8, project=runs/train-cls, name=exp, exist_ok=False, pretrained=True, optimizer=Adam, lr0=0.001, decay=5e-05, label_smoothing=0.1, cutoff=None, dropout=None, verbose=False, seed=0, local_rank=-1\n", + "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", + "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-cls', view at http://localhost:6006/\n", + "\n", + "Dataset not found ⚠️, missing path /content/datasets/imagenette160, attempting download...\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenette160.zip to /content/datasets/imagenette160.zip...\n", + "100% 103M/103M [00:00<00:00, 347MB/s] \n", + "Unzipping /content/datasets/imagenette160.zip...\n", + "Dataset download success ✅ (3.3s), saved to \u001b[1m/content/datasets/imagenette160\u001b[0m\n", + "\n", + "\u001b[34m\u001b[1malbumentations: \u001b[0mRandomResizedCrop(p=1.0, height=224, width=224, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1), HorizontalFlip(p=0.5), ColorJitter(p=0.5, brightness=[0.6, 1.4], contrast=[0.6, 1.4], saturation=[0.6, 1.4], hue=[0, 0]), Normalize(p=1.0, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), max_pixel_value=255.0), ToTensorV2(always_apply=True, p=1.0, transpose_mask=False)\n", + "Model summary: 149 layers, 4185290 parameters, 4185290 gradients, 10.5 GFLOPs\n", + "\u001b[34m\u001b[1moptimizer:\u001b[0m Adam(lr=0.001) with parameter groups 32 weight(decay=0.0), 33 weight(decay=5e-05), 33 bias\n", + "Image sizes 224 train, 224 test\n", + "Using 1 dataloader workers\n", + "Logging results to \u001b[1mruns/train-cls/exp\u001b[0m\n", + "Starting yolov5s-cls.pt training on imagenette160 dataset with 10 classes for 5 epochs...\n", + "\n", + " Epoch GPU_mem train_loss val_loss top1_acc top5_acc\n", + " 1/5 1.47G 1.05 0.974 0.828 0.975: 100% 148/148 [00:38<00:00, 3.82it/s]\n", + " 2/5 1.73G 0.895 0.766 0.911 0.994: 100% 148/148 [00:36<00:00, 4.03it/s]\n", + " 3/5 1.73G 0.82 0.704 0.934 0.996: 100% 148/148 [00:35<00:00, 4.20it/s]\n", + " 4/5 1.73G 0.766 0.664 0.951 0.998: 100% 148/148 [00:36<00:00, 4.05it/s]\n", + " 5/5 1.73G 0.724 0.634 0.959 0.997: 100% 148/148 [00:37<00:00, 3.94it/s]\n", + "\n", + "Training complete (0.052 hours)\n", + "Results saved to \u001b[1mruns/train-cls/exp\u001b[0m\n", + "Predict: python classify/predict.py --weights runs/train-cls/exp/weights/best.pt --source im.jpg\n", + "Validate: python classify/val.py --weights runs/train-cls/exp/weights/best.pt --data /content/datasets/imagenette160\n", + "Export: python export.py --weights runs/train-cls/exp/weights/best.pt --include onnx\n", + "PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', 'runs/train-cls/exp/weights/best.pt')\n", + "Visualize: https://netron.app\n", + "\n" + ] + } + ], + "source": [ + "# Train YOLOv5s Classification on Imagenette160 for 3 epochs\n", + "!python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224 --cache" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "15glLzbQx5u0" + }, + "source": [ + "# 4. Visualize" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nWOsI5wJR1o3" + }, + "source": [ + "## Comet Logging and Visualization 🌟 NEW\n", + "\n", + "[Comet](https://www.comet.com/site/lp/yolov5-with-comet/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!\n", + "\n", + "Getting started is easy:\n", + "```shell\n", + "pip install comet_ml # 1. install\n", + "export COMET_API_KEY= # 2. paste API key\n", + "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n", + "```\n", + "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n", + "\n", + "\n", + "\"Comet" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Lay2WsTjNJzP" + }, + "source": [ + "## ClearML Logging and Automation 🌟 NEW\n", + "\n", + "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n", + "\n", + "- `pip install clearml`\n", + "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n", + "\n", + "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n", + "\n", + "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n", + "\n", + "\n", + "\"ClearML" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-WPvRbS5Swl6" + }, + "source": [ + "## Local Logging\n", + "\n", + "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n", + "\n", + "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n", + "\n", + "\"Local\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zelyeqbyt3GD" + }, + "source": [ + "# Environments\n", + "\n", + "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n", + "\n", + "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", + "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n", + "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n", + "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \"Docker\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Qu7Iesl0p54" + }, + "source": [ + "# Status\n", + "\n", + "![YOLOv5 CI](https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg)\n", + "\n", + "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IEijrePND_2I" + }, + "source": [ + "# Appendix\n", + "\n", + "Additional content below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GMusP4OAxFu6" + }, + "outputs": [], + "source": [ + "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n", + "import torch\n", + "\n", + "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True, trust_repo=True) # or yolov5n - yolov5x6 or custom\n", + "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n", + "results = model(im) # inference\n", + "results.print() # or .show(), .save(), .crop(), .pandas(), etc." + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "YOLOv5 Classification Tutorial", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/yolov5/classify/val.py b/yolov5/classify/val.py new file mode 100644 index 0000000000000000000000000000000000000000..8ce48f0645bf7252f95f72ec867f30d44f78341d --- /dev/null +++ b/yolov5/classify/val.py @@ -0,0 +1,178 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +""" +Validate a trained YOLOv5 classification model on a classification dataset. + +Usage: + $ bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images) + $ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ImageNet + +Usage - formats: + $ python classify/val.py --weights yolov5s-cls.pt # PyTorch + yolov5s-cls.torchscript # TorchScript + yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s-cls_openvino_model # OpenVINO + yolov5s-cls.engine # TensorRT + yolov5s-cls.mlmodel # CoreML (macOS-only) + yolov5s-cls_saved_model # TensorFlow SavedModel + yolov5s-cls.pb # TensorFlow GraphDef + yolov5s-cls.tflite # TensorFlow Lite + yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU + yolov5s-cls_paddle_model # PaddlePaddle +""" + +import argparse +import os +import sys +from pathlib import Path + +import torch +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import DetectMultiBackend +from utils.dataloaders import create_classification_dataloader +from utils.general import ( + LOGGER, + TQDM_BAR_FORMAT, + Profile, + check_img_size, + check_requirements, + colorstr, + increment_path, + print_args, +) +from utils.torch_utils import select_device, smart_inference_mode + + +@smart_inference_mode() +def run( + data=ROOT / "../datasets/mnist", # dataset dir + weights=ROOT / "yolov5s-cls.pt", # model.pt path(s) + batch_size=128, # batch size + imgsz=224, # inference size (pixels) + device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu + workers=8, # max dataloader workers (per RANK in DDP mode) + verbose=False, # verbose output + project=ROOT / "runs/val-cls", # save to project/name + name="exp", # save to project/name + exist_ok=False, # existing project/name ok, do not increment + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + model=None, + dataloader=None, + criterion=None, + pbar=None, +): + """Validates a YOLOv5 classification model on a dataset, computing metrics like top1 and top5 accuracy.""" + # Initialize/load model and set device + training = model is not None + if training: # called by train.py + device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model + half &= device.type != "cpu" # half precision only supported on CUDA + model.half() if half else model.float() + else: # called directly + device = select_device(device, batch_size=batch_size) + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + save_dir.mkdir(parents=True, exist_ok=True) # make dir + + # Load model + model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half) + stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine + imgsz = check_img_size(imgsz, s=stride) # check image size + half = model.fp16 # FP16 supported on limited backends with CUDA + if engine: + batch_size = model.batch_size + else: + device = model.device + if not (pt or jit): + batch_size = 1 # export.py models default to batch-size 1 + LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models") + + # Dataloader + data = Path(data) + test_dir = data / "test" if (data / "test").exists() else data / "val" # data/test or data/val + dataloader = create_classification_dataloader( + path=test_dir, imgsz=imgsz, batch_size=batch_size, augment=False, rank=-1, workers=workers + ) + + model.eval() + pred, targets, loss, dt = [], [], 0, (Profile(device=device), Profile(device=device), Profile(device=device)) + n = len(dataloader) # number of batches + action = "validating" if dataloader.dataset.root.stem == "val" else "testing" + desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}" + bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0) + with torch.cuda.amp.autocast(enabled=device.type != "cpu"): + for images, labels in bar: + with dt[0]: + images, labels = images.to(device, non_blocking=True), labels.to(device) + + with dt[1]: + y = model(images) + + with dt[2]: + pred.append(y.argsort(1, descending=True)[:, :5]) + targets.append(labels) + if criterion: + loss += criterion(y, labels) + + loss /= n + pred, targets = torch.cat(pred), torch.cat(targets) + correct = (targets[:, None] == pred).float() + acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy + top1, top5 = acc.mean(0).tolist() + + if pbar: + pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}" + if verbose: # all classes + LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}") + LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}") + for i, c in model.names.items(): + acc_i = acc[targets == i] + top1i, top5i = acc_i.mean(0).tolist() + LOGGER.info(f"{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}") + + # Print results + t = tuple(x.t / len(dataloader.dataset.samples) * 1e3 for x in dt) # speeds per image + shape = (1, 3, imgsz, imgsz) + LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}" % t) + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}") + + return top1, top5, loss + + +def parse_opt(): + """Parses and returns command line arguments for YOLOv5 model evaluation and inference settings.""" + parser = argparse.ArgumentParser() + parser.add_argument("--data", type=str, default=ROOT / "../datasets/mnist", help="dataset path") + parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-cls.pt", help="model.pt path(s)") + parser.add_argument("--batch-size", type=int, default=128, help="batch size") + parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=224, help="inference size (pixels)") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") + parser.add_argument("--verbose", nargs="?", const=True, default=True, help="verbose output") + parser.add_argument("--project", default=ROOT / "runs/val-cls", help="save to project/name") + parser.add_argument("--name", default="exp", help="save to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") + parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") + opt = parser.parse_args() + print_args(vars(opt)) + return opt + + +def main(opt): + """Executes the YOLOv5 model prediction workflow, handling argument parsing and requirement checks.""" + check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) + run(**vars(opt)) + + +if __name__ == "__main__": + opt = parse_opt() + main(opt) diff --git a/yolov5/data/Argoverse.yaml b/yolov5/data/Argoverse.yaml new file mode 100644 index 0000000000000000000000000000000000000000..366552ea4f311ef32b1fdcf4f14e99031694da6c --- /dev/null +++ b/yolov5/data/Argoverse.yaml @@ -0,0 +1,72 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI +# Example usage: python train.py --data Argoverse.yaml +# parent +# ├── yolov5 +# └── datasets +# └── Argoverse ← downloads here (31.3 GB) + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/Argoverse # dataset root dir +train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images +val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images +test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview + +# Classes +names: + 0: person + 1: bicycle + 2: car + 3: motorcycle + 4: bus + 5: truck + 6: traffic_light + 7: stop_sign + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + import json + + from tqdm import tqdm + from utils.general import download, Path + + + def argoverse2yolo(set): + labels = {} + a = json.load(open(set, "rb")) + for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."): + img_id = annot['image_id'] + img_name = a['images'][img_id]['name'] + img_label_name = f'{img_name[:-3]}txt' + + cls = annot['category_id'] # instance class id + x_center, y_center, width, height = annot['bbox'] + x_center = (x_center + width / 2) / 1920.0 # offset and scale + y_center = (y_center + height / 2) / 1200.0 # offset and scale + width /= 1920.0 # scale + height /= 1200.0 # scale + + img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']] + if not img_dir.exists(): + img_dir.mkdir(parents=True, exist_ok=True) + + k = str(img_dir / img_label_name) + if k not in labels: + labels[k] = [] + labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n") + + for k in labels: + with open(k, "w") as f: + f.writelines(labels[k]) + + + # Download + dir = Path(yaml['path']) # dataset root dir + urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip'] + download(urls, dir=dir, delete=False) + + # Convert + annotations_dir = 'Argoverse-HD/annotations/' + (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images' + for d in "train.json", "val.json": + argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels diff --git a/yolov5/data/GlobalWheat2020.yaml b/yolov5/data/GlobalWheat2020.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2039ccd3cce807627fdf0bc8f229ab00770f1407 --- /dev/null +++ b/yolov5/data/GlobalWheat2020.yaml @@ -0,0 +1,52 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan +# Example usage: python train.py --data GlobalWheat2020.yaml +# parent +# ├── yolov5 +# └── datasets +# └── GlobalWheat2020 ← downloads here (7.0 GB) + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/GlobalWheat2020 # dataset root dir +train: # train images (relative to 'path') 3422 images + - images/arvalis_1 + - images/arvalis_2 + - images/arvalis_3 + - images/ethz_1 + - images/rres_1 + - images/inrae_1 + - images/usask_1 +val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1) + - images/ethz_1 +test: # test images (optional) 1276 images + - images/utokyo_1 + - images/utokyo_2 + - images/nau_1 + - images/uq_1 + +# Classes +names: + 0: wheat_head + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + from utils.general import download, Path + + + # Download + dir = Path(yaml['path']) # dataset root dir + urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip', + 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip'] + download(urls, dir=dir) + + # Make Directories + for p in 'annotations', 'images', 'labels': + (dir / p).mkdir(parents=True, exist_ok=True) + + # Move + for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \ + 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1': + (dir / p).rename(dir / 'images' / p) # move to /images + f = (dir / p).with_suffix('.json') # json file + if f.exists(): + f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations diff --git a/yolov5/data/ImageNet.yaml b/yolov5/data/ImageNet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..979a0e4de3375cb7ed7e75e3039d9b30ae2972d3 --- /dev/null +++ b/yolov5/data/ImageNet.yaml @@ -0,0 +1,1020 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University +# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels +# Example usage: python classify/train.py --data imagenet +# parent +# ├── yolov5 +# └── datasets +# └── imagenet ← downloads here (144 GB) + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/imagenet # dataset root dir +train: train # train images (relative to 'path') 1281167 images +val: val # val images (relative to 'path') 50000 images +test: # test images (optional) + +# Classes +names: + 0: tench + 1: goldfish + 2: great white shark + 3: tiger shark + 4: hammerhead shark + 5: electric ray + 6: stingray + 7: cock + 8: hen + 9: ostrich + 10: brambling + 11: goldfinch + 12: house finch + 13: junco + 14: indigo bunting + 15: American robin + 16: bulbul + 17: jay + 18: magpie + 19: chickadee + 20: American dipper + 21: kite + 22: bald eagle + 23: vulture + 24: great grey owl + 25: fire salamander + 26: smooth newt + 27: newt + 28: spotted salamander + 29: axolotl + 30: American bullfrog + 31: tree frog + 32: tailed frog + 33: loggerhead sea turtle + 34: leatherback sea turtle + 35: mud turtle + 36: terrapin + 37: box turtle + 38: banded gecko + 39: green iguana + 40: Carolina anole + 41: desert grassland whiptail lizard + 42: agama + 43: frilled-necked lizard + 44: alligator lizard + 45: Gila monster + 46: European green lizard + 47: chameleon + 48: Komodo dragon + 49: Nile crocodile + 50: American alligator + 51: triceratops + 52: worm snake + 53: ring-necked snake + 54: eastern hog-nosed snake + 55: smooth green snake + 56: kingsnake + 57: garter snake + 58: water snake + 59: vine snake + 60: night snake + 61: boa constrictor + 62: African rock python + 63: Indian cobra + 64: green mamba + 65: sea snake + 66: Saharan horned viper + 67: eastern diamondback rattlesnake + 68: sidewinder + 69: trilobite + 70: harvestman + 71: scorpion + 72: yellow garden spider + 73: barn spider + 74: European garden spider + 75: southern black widow + 76: tarantula + 77: wolf spider + 78: tick + 79: centipede + 80: black grouse + 81: ptarmigan + 82: ruffed grouse + 83: prairie grouse + 84: peacock + 85: quail + 86: partridge + 87: grey parrot + 88: macaw + 89: sulphur-crested cockatoo + 90: lorikeet + 91: coucal + 92: bee eater + 93: hornbill + 94: hummingbird + 95: jacamar + 96: toucan + 97: duck + 98: red-breasted merganser + 99: goose + 100: black swan + 101: tusker + 102: echidna + 103: platypus + 104: wallaby + 105: koala + 106: wombat + 107: jellyfish + 108: sea anemone + 109: brain coral + 110: flatworm + 111: nematode + 112: conch + 113: snail + 114: slug + 115: sea slug + 116: chiton + 117: chambered nautilus + 118: Dungeness crab + 119: rock crab + 120: fiddler crab + 121: red king crab + 122: American lobster + 123: spiny lobster + 124: crayfish + 125: hermit crab + 126: isopod + 127: white stork + 128: black stork + 129: spoonbill + 130: flamingo + 131: little blue heron + 132: great egret + 133: bittern + 134: crane (bird) + 135: limpkin + 136: common gallinule + 137: American coot + 138: bustard + 139: ruddy turnstone + 140: dunlin + 141: common redshank + 142: dowitcher + 143: oystercatcher + 144: pelican + 145: king penguin + 146: albatross + 147: grey whale + 148: killer whale + 149: dugong + 150: sea lion + 151: Chihuahua + 152: Japanese Chin + 153: Maltese + 154: Pekingese + 155: Shih Tzu + 156: King Charles Spaniel + 157: Papillon + 158: toy terrier + 159: Rhodesian Ridgeback + 160: Afghan Hound + 161: Basset Hound + 162: Beagle + 163: Bloodhound + 164: Bluetick Coonhound + 165: Black and Tan Coonhound + 166: Treeing Walker Coonhound + 167: English foxhound + 168: Redbone Coonhound + 169: borzoi + 170: Irish Wolfhound + 171: Italian Greyhound + 172: Whippet + 173: Ibizan Hound + 174: Norwegian Elkhound + 175: Otterhound + 176: Saluki + 177: Scottish Deerhound + 178: Weimaraner + 179: Staffordshire Bull Terrier + 180: American Staffordshire Terrier + 181: Bedlington Terrier + 182: Border Terrier + 183: Kerry Blue Terrier + 184: Irish Terrier + 185: Norfolk Terrier + 186: Norwich Terrier + 187: Yorkshire Terrier + 188: Wire Fox Terrier + 189: Lakeland Terrier + 190: Sealyham Terrier + 191: Airedale Terrier + 192: Cairn Terrier + 193: Australian Terrier + 194: Dandie Dinmont Terrier + 195: Boston Terrier + 196: Miniature Schnauzer + 197: Giant Schnauzer + 198: Standard Schnauzer + 199: Scottish Terrier + 200: Tibetan Terrier + 201: Australian Silky Terrier + 202: Soft-coated Wheaten Terrier + 203: West Highland White Terrier + 204: Lhasa Apso + 205: Flat-Coated Retriever + 206: Curly-coated Retriever + 207: Golden Retriever + 208: Labrador Retriever + 209: Chesapeake Bay Retriever + 210: German Shorthaired Pointer + 211: Vizsla + 212: English Setter + 213: Irish Setter + 214: Gordon Setter + 215: Brittany + 216: Clumber Spaniel + 217: English Springer Spaniel + 218: Welsh Springer Spaniel + 219: Cocker Spaniels + 220: Sussex Spaniel + 221: Irish Water Spaniel + 222: Kuvasz + 223: Schipperke + 224: Groenendael + 225: Malinois + 226: Briard + 227: Australian Kelpie + 228: Komondor + 229: Old English Sheepdog + 230: Shetland Sheepdog + 231: collie + 232: Border Collie + 233: Bouvier des Flandres + 234: Rottweiler + 235: German Shepherd Dog + 236: Dobermann + 237: Miniature Pinscher + 238: Greater Swiss Mountain Dog + 239: Bernese Mountain Dog + 240: Appenzeller Sennenhund + 241: Entlebucher Sennenhund + 242: Boxer + 243: Bullmastiff + 244: Tibetan Mastiff + 245: French Bulldog + 246: Great Dane + 247: St. Bernard + 248: husky + 249: Alaskan Malamute + 250: Siberian Husky + 251: Dalmatian + 252: Affenpinscher + 253: Basenji + 254: pug + 255: Leonberger + 256: Newfoundland + 257: Pyrenean Mountain Dog + 258: Samoyed + 259: Pomeranian + 260: Chow Chow + 261: Keeshond + 262: Griffon Bruxellois + 263: Pembroke Welsh Corgi + 264: Cardigan Welsh Corgi + 265: Toy Poodle + 266: Miniature Poodle + 267: Standard Poodle + 268: Mexican hairless dog + 269: grey wolf + 270: Alaskan tundra wolf + 271: red wolf + 272: coyote + 273: dingo + 274: dhole + 275: African wild dog + 276: hyena + 277: red fox + 278: kit fox + 279: Arctic fox + 280: grey fox + 281: tabby cat + 282: tiger cat + 283: Persian cat + 284: Siamese cat + 285: Egyptian Mau + 286: cougar + 287: lynx + 288: leopard + 289: snow leopard + 290: jaguar + 291: lion + 292: tiger + 293: cheetah + 294: brown bear + 295: American black bear + 296: polar bear + 297: sloth bear + 298: mongoose + 299: meerkat + 300: tiger beetle + 301: ladybug + 302: ground beetle + 303: longhorn beetle + 304: leaf beetle + 305: dung beetle + 306: rhinoceros beetle + 307: weevil + 308: fly + 309: bee + 310: ant + 311: grasshopper + 312: cricket + 313: stick insect + 314: cockroach + 315: mantis + 316: cicada + 317: leafhopper + 318: lacewing + 319: dragonfly + 320: damselfly + 321: red admiral + 322: ringlet + 323: monarch butterfly + 324: small white + 325: sulphur butterfly + 326: gossamer-winged butterfly + 327: starfish + 328: sea urchin + 329: sea cucumber + 330: cottontail rabbit + 331: hare + 332: Angora rabbit + 333: hamster + 334: porcupine + 335: fox squirrel + 336: marmot + 337: beaver + 338: guinea pig + 339: common sorrel + 340: zebra + 341: pig + 342: wild boar + 343: warthog + 344: hippopotamus + 345: ox + 346: water buffalo + 347: bison + 348: ram + 349: bighorn sheep + 350: Alpine ibex + 351: hartebeest + 352: impala + 353: gazelle + 354: dromedary + 355: llama + 356: weasel + 357: mink + 358: European polecat + 359: black-footed ferret + 360: otter + 361: skunk + 362: badger + 363: armadillo + 364: three-toed sloth + 365: orangutan + 366: gorilla + 367: chimpanzee + 368: gibbon + 369: siamang + 370: guenon + 371: patas monkey + 372: baboon + 373: macaque + 374: langur + 375: black-and-white colobus + 376: proboscis monkey + 377: marmoset + 378: white-headed capuchin + 379: howler monkey + 380: titi + 381: Geoffroy's spider monkey + 382: common squirrel monkey + 383: ring-tailed lemur + 384: indri + 385: Asian elephant + 386: African bush elephant + 387: red panda + 388: giant panda + 389: snoek + 390: eel + 391: coho salmon + 392: rock beauty + 393: clownfish + 394: sturgeon + 395: garfish + 396: lionfish + 397: pufferfish + 398: abacus + 399: abaya + 400: academic gown + 401: accordion + 402: acoustic guitar + 403: aircraft carrier + 404: airliner + 405: airship + 406: altar + 407: ambulance + 408: amphibious vehicle + 409: analog clock + 410: apiary + 411: apron + 412: waste container + 413: assault rifle + 414: backpack + 415: bakery + 416: balance beam + 417: balloon + 418: ballpoint pen + 419: Band-Aid + 420: banjo + 421: baluster + 422: barbell + 423: barber chair + 424: barbershop + 425: barn + 426: barometer + 427: barrel + 428: wheelbarrow + 429: baseball + 430: basketball + 431: bassinet + 432: bassoon + 433: swimming cap + 434: bath towel + 435: bathtub + 436: station wagon + 437: lighthouse + 438: beaker + 439: military cap + 440: beer bottle + 441: beer glass + 442: bell-cot + 443: bib + 444: tandem bicycle + 445: bikini + 446: ring binder + 447: binoculars + 448: birdhouse + 449: boathouse + 450: bobsleigh + 451: bolo tie + 452: poke bonnet + 453: bookcase + 454: bookstore + 455: bottle cap + 456: bow + 457: bow tie + 458: brass + 459: bra + 460: breakwater + 461: breastplate + 462: broom + 463: bucket + 464: buckle + 465: bulletproof vest + 466: high-speed train + 467: butcher shop + 468: taxicab + 469: cauldron + 470: candle + 471: cannon + 472: canoe + 473: can opener + 474: cardigan + 475: car mirror + 476: carousel + 477: tool kit + 478: carton + 479: car wheel + 480: automated teller machine + 481: cassette + 482: cassette player + 483: castle + 484: catamaran + 485: CD player + 486: cello + 487: mobile phone + 488: chain + 489: chain-link fence + 490: chain mail + 491: chainsaw + 492: chest + 493: chiffonier + 494: chime + 495: china cabinet + 496: Christmas stocking + 497: church + 498: movie theater + 499: cleaver + 500: cliff dwelling + 501: cloak + 502: clogs + 503: cocktail shaker + 504: coffee mug + 505: coffeemaker + 506: coil + 507: combination lock + 508: computer keyboard + 509: confectionery store + 510: container ship + 511: convertible + 512: corkscrew + 513: cornet + 514: cowboy boot + 515: cowboy hat + 516: cradle + 517: crane (machine) + 518: crash helmet + 519: crate + 520: infant bed + 521: Crock Pot + 522: croquet ball + 523: crutch + 524: cuirass + 525: dam + 526: desk + 527: desktop computer + 528: rotary dial telephone + 529: diaper + 530: digital clock + 531: digital watch + 532: dining table + 533: dishcloth + 534: dishwasher + 535: disc brake + 536: dock + 537: dog sled + 538: dome + 539: doormat + 540: drilling rig + 541: drum + 542: drumstick + 543: dumbbell + 544: Dutch oven + 545: electric fan + 546: electric guitar + 547: electric locomotive + 548: entertainment center + 549: envelope + 550: espresso machine + 551: face powder + 552: feather boa + 553: filing cabinet + 554: fireboat + 555: fire engine + 556: fire screen sheet + 557: flagpole + 558: flute + 559: folding chair + 560: football helmet + 561: forklift + 562: fountain + 563: fountain pen + 564: four-poster bed + 565: freight car + 566: French horn + 567: frying pan + 568: fur coat + 569: garbage truck + 570: gas mask + 571: gas pump + 572: goblet + 573: go-kart + 574: golf ball + 575: golf cart + 576: gondola + 577: gong + 578: gown + 579: grand piano + 580: greenhouse + 581: grille + 582: grocery store + 583: guillotine + 584: barrette + 585: hair spray + 586: half-track + 587: hammer + 588: hamper + 589: hair dryer + 590: hand-held computer + 591: handkerchief + 592: hard disk drive + 593: harmonica + 594: harp + 595: harvester + 596: hatchet + 597: holster + 598: home theater + 599: honeycomb + 600: hook + 601: hoop skirt + 602: horizontal bar + 603: horse-drawn vehicle + 604: hourglass + 605: iPod + 606: clothes iron + 607: jack-o'-lantern + 608: jeans + 609: jeep + 610: T-shirt + 611: jigsaw puzzle + 612: pulled rickshaw + 613: joystick + 614: kimono + 615: knee pad + 616: knot + 617: lab coat + 618: ladle + 619: lampshade + 620: laptop computer + 621: lawn mower + 622: lens cap + 623: paper knife + 624: library + 625: lifeboat + 626: lighter + 627: limousine + 628: ocean liner + 629: lipstick + 630: slip-on shoe + 631: lotion + 632: speaker + 633: loupe + 634: sawmill + 635: magnetic compass + 636: mail bag + 637: mailbox + 638: tights + 639: tank suit + 640: manhole cover + 641: maraca + 642: marimba + 643: mask + 644: match + 645: maypole + 646: maze + 647: measuring cup + 648: medicine chest + 649: megalith + 650: microphone + 651: microwave oven + 652: military uniform + 653: milk can + 654: minibus + 655: miniskirt + 656: minivan + 657: missile + 658: mitten + 659: mixing bowl + 660: mobile home + 661: Model T + 662: modem + 663: monastery + 664: monitor + 665: moped + 666: mortar + 667: square academic cap + 668: mosque + 669: mosquito net + 670: scooter + 671: mountain bike + 672: tent + 673: computer mouse + 674: mousetrap + 675: moving van + 676: muzzle + 677: nail + 678: neck brace + 679: necklace + 680: nipple + 681: notebook computer + 682: obelisk + 683: oboe + 684: ocarina + 685: odometer + 686: oil filter + 687: organ + 688: oscilloscope + 689: overskirt + 690: bullock cart + 691: oxygen mask + 692: packet + 693: paddle + 694: paddle wheel + 695: padlock + 696: paintbrush + 697: pajamas + 698: palace + 699: pan flute + 700: paper towel + 701: parachute + 702: parallel bars + 703: park bench + 704: parking meter + 705: passenger car + 706: patio + 707: payphone + 708: pedestal + 709: pencil case + 710: pencil sharpener + 711: perfume + 712: Petri dish + 713: photocopier + 714: plectrum + 715: Pickelhaube + 716: picket fence + 717: pickup truck + 718: pier + 719: piggy bank + 720: pill bottle + 721: pillow + 722: ping-pong ball + 723: pinwheel + 724: pirate ship + 725: pitcher + 726: hand plane + 727: planetarium + 728: plastic bag + 729: plate rack + 730: plow + 731: plunger + 732: Polaroid camera + 733: pole + 734: police van + 735: poncho + 736: billiard table + 737: soda bottle + 738: pot + 739: potter's wheel + 740: power drill + 741: prayer rug + 742: printer + 743: prison + 744: projectile + 745: projector + 746: hockey puck + 747: punching bag + 748: purse + 749: quill + 750: quilt + 751: race car + 752: racket + 753: radiator + 754: radio + 755: radio telescope + 756: rain barrel + 757: recreational vehicle + 758: reel + 759: reflex camera + 760: refrigerator + 761: remote control + 762: restaurant + 763: revolver + 764: rifle + 765: rocking chair + 766: rotisserie + 767: eraser + 768: rugby ball + 769: ruler + 770: running shoe + 771: safe + 772: safety pin + 773: salt shaker + 774: sandal + 775: sarong + 776: saxophone + 777: scabbard + 778: weighing scale + 779: school bus + 780: schooner + 781: scoreboard + 782: CRT screen + 783: screw + 784: screwdriver + 785: seat belt + 786: sewing machine + 787: shield + 788: shoe store + 789: shoji + 790: shopping basket + 791: shopping cart + 792: shovel + 793: shower cap + 794: shower curtain + 795: ski + 796: ski mask + 797: sleeping bag + 798: slide rule + 799: sliding door + 800: slot machine + 801: snorkel + 802: snowmobile + 803: snowplow + 804: soap dispenser + 805: soccer ball + 806: sock + 807: solar thermal collector + 808: sombrero + 809: soup bowl + 810: space bar + 811: space heater + 812: space shuttle + 813: spatula + 814: motorboat + 815: spider web + 816: spindle + 817: sports car + 818: spotlight + 819: stage + 820: steam locomotive + 821: through arch bridge + 822: steel drum + 823: stethoscope + 824: scarf + 825: stone wall + 826: stopwatch + 827: stove + 828: strainer + 829: tram + 830: stretcher + 831: couch + 832: stupa + 833: submarine + 834: suit + 835: sundial + 836: sunglass + 837: sunglasses + 838: sunscreen + 839: suspension bridge + 840: mop + 841: sweatshirt + 842: swimsuit + 843: swing + 844: switch + 845: syringe + 846: table lamp + 847: tank + 848: tape player + 849: teapot + 850: teddy bear + 851: television + 852: tennis ball + 853: thatched roof + 854: front curtain + 855: thimble + 856: threshing machine + 857: throne + 858: tile roof + 859: toaster + 860: tobacco shop + 861: toilet seat + 862: torch + 863: totem pole + 864: tow truck + 865: toy store + 866: tractor + 867: semi-trailer truck + 868: tray + 869: trench coat + 870: tricycle + 871: trimaran + 872: tripod + 873: triumphal arch + 874: trolleybus + 875: trombone + 876: tub + 877: turnstile + 878: typewriter keyboard + 879: umbrella + 880: unicycle + 881: upright piano + 882: vacuum cleaner + 883: vase + 884: vault + 885: velvet + 886: vending machine + 887: vestment + 888: viaduct + 889: violin + 890: volleyball + 891: waffle iron + 892: wall clock + 893: wallet + 894: wardrobe + 895: military aircraft + 896: sink + 897: washing machine + 898: water bottle + 899: water jug + 900: water tower + 901: whiskey jug + 902: whistle + 903: wig + 904: window screen + 905: window shade + 906: Windsor tie + 907: wine bottle + 908: wing + 909: wok + 910: wooden spoon + 911: wool + 912: split-rail fence + 913: shipwreck + 914: yawl + 915: yurt + 916: website + 917: comic book + 918: crossword + 919: traffic sign + 920: traffic light + 921: dust jacket + 922: menu + 923: plate + 924: guacamole + 925: consomme + 926: hot pot + 927: trifle + 928: ice cream + 929: ice pop + 930: baguette + 931: bagel + 932: pretzel + 933: cheeseburger + 934: hot dog + 935: mashed potato + 936: cabbage + 937: broccoli + 938: cauliflower + 939: zucchini + 940: spaghetti squash + 941: acorn squash + 942: butternut squash + 943: cucumber + 944: artichoke + 945: bell pepper + 946: cardoon + 947: mushroom + 948: Granny Smith + 949: strawberry + 950: orange + 951: lemon + 952: fig + 953: pineapple + 954: banana + 955: jackfruit + 956: custard apple + 957: pomegranate + 958: hay + 959: carbonara + 960: chocolate syrup + 961: dough + 962: meatloaf + 963: pizza + 964: pot pie + 965: burrito + 966: red wine + 967: espresso + 968: cup + 969: eggnog + 970: alp + 971: bubble + 972: cliff + 973: coral reef + 974: geyser + 975: lakeshore + 976: promontory + 977: shoal + 978: seashore + 979: valley + 980: volcano + 981: baseball player + 982: bridegroom + 983: scuba diver + 984: rapeseed + 985: daisy + 986: yellow lady's slipper + 987: corn + 988: acorn + 989: rose hip + 990: horse chestnut seed + 991: coral fungus + 992: agaric + 993: gyromitra + 994: stinkhorn mushroom + 995: earth star + 996: hen-of-the-woods + 997: bolete + 998: ear + 999: toilet paper + +# Download script/URL (optional) +download: data/scripts/get_imagenet.sh diff --git a/yolov5/data/ImageNet10.yaml b/yolov5/data/ImageNet10.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2189def7d4576d72d0d02dd22d55bdc52c35f60c --- /dev/null +++ b/yolov5/data/ImageNet10.yaml @@ -0,0 +1,30 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University +# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels +# Example usage: python classify/train.py --data imagenet +# parent +# ├── yolov5 +# └── datasets +# └── imagenet10 ← downloads here + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/imagenet10 # dataset root dir +train: train # train images (relative to 'path') 1281167 images +val: val # val images (relative to 'path') 50000 images +test: # test images (optional) + +# Classes +names: + 0: tench + 1: goldfish + 2: great white shark + 3: tiger shark + 4: hammerhead shark + 5: electric ray + 6: stingray + 7: cock + 8: hen + 9: ostrich + +# Download script/URL (optional) +download: data/scripts/get_imagenet10.sh diff --git a/yolov5/data/ImageNet100.yaml b/yolov5/data/ImageNet100.yaml new file mode 100644 index 0000000000000000000000000000000000000000..560cdecdbae90acd9db153d802426866912a28ad --- /dev/null +++ b/yolov5/data/ImageNet100.yaml @@ -0,0 +1,119 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University +# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels +# Example usage: python classify/train.py --data imagenet +# parent +# ├── yolov5 +# └── datasets +# └── imagenet100 ← downloads here + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/imagenet100 # dataset root dir +train: train # train images (relative to 'path') 1281167 images +val: val # val images (relative to 'path') 50000 images +test: # test images (optional) + +# Classes +names: + 0: tench + 1: goldfish + 2: great white shark + 3: tiger shark + 4: hammerhead shark + 5: electric ray + 6: stingray + 7: cock + 8: hen + 9: ostrich + 10: brambling + 11: goldfinch + 12: house finch + 13: junco + 14: indigo bunting + 15: American robin + 16: bulbul + 17: jay + 18: magpie + 19: chickadee + 20: American dipper + 21: kite + 22: bald eagle + 23: vulture + 24: great grey owl + 25: fire salamander + 26: smooth newt + 27: newt + 28: spotted salamander + 29: axolotl + 30: American bullfrog + 31: tree frog + 32: tailed frog + 33: loggerhead sea turtle + 34: leatherback sea turtle + 35: mud turtle + 36: terrapin + 37: box turtle + 38: banded gecko + 39: green iguana + 40: Carolina anole + 41: desert grassland whiptail lizard + 42: agama + 43: frilled-necked lizard + 44: alligator lizard + 45: Gila monster + 46: European green lizard + 47: chameleon + 48: Komodo dragon + 49: Nile crocodile + 50: American alligator + 51: triceratops + 52: worm snake + 53: ring-necked snake + 54: eastern hog-nosed snake + 55: smooth green snake + 56: kingsnake + 57: garter snake + 58: water snake + 59: vine snake + 60: night snake + 61: boa constrictor + 62: African rock python + 63: Indian cobra + 64: green mamba + 65: sea snake + 66: Saharan horned viper + 67: eastern diamondback rattlesnake + 68: sidewinder + 69: trilobite + 70: harvestman + 71: scorpion + 72: yellow garden spider + 73: barn spider + 74: European garden spider + 75: southern black widow + 76: tarantula + 77: wolf spider + 78: tick + 79: centipede + 80: black grouse + 81: ptarmigan + 82: ruffed grouse + 83: prairie grouse + 84: peacock + 85: quail + 86: partridge + 87: grey parrot + 88: macaw + 89: sulphur-crested cockatoo + 90: lorikeet + 91: coucal + 92: bee eater + 93: hornbill + 94: hummingbird + 95: jacamar + 96: toucan + 97: duck + 98: red-breasted merganser + 99: goose +# Download script/URL (optional) +download: data/scripts/get_imagenet100.sh diff --git a/yolov5/data/ImageNet1000.yaml b/yolov5/data/ImageNet1000.yaml new file mode 100644 index 0000000000000000000000000000000000000000..aa17e9e059cf34a1d0c6787db3299b2a71fe3329 --- /dev/null +++ b/yolov5/data/ImageNet1000.yaml @@ -0,0 +1,1020 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University +# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels +# Example usage: python classify/train.py --data imagenet +# parent +# ├── yolov5 +# └── datasets +# └── imagenet100 ← downloads here + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/imagenet1000 # dataset root dir +train: train # train images (relative to 'path') 1281167 images +val: val # val images (relative to 'path') 50000 images +test: # test images (optional) + +# Classes +names: + 0: tench + 1: goldfish + 2: great white shark + 3: tiger shark + 4: hammerhead shark + 5: electric ray + 6: stingray + 7: cock + 8: hen + 9: ostrich + 10: brambling + 11: goldfinch + 12: house finch + 13: junco + 14: indigo bunting + 15: American robin + 16: bulbul + 17: jay + 18: magpie + 19: chickadee + 20: American dipper + 21: kite + 22: bald eagle + 23: vulture + 24: great grey owl + 25: fire salamander + 26: smooth newt + 27: newt + 28: spotted salamander + 29: axolotl + 30: American bullfrog + 31: tree frog + 32: tailed frog + 33: loggerhead sea turtle + 34: leatherback sea turtle + 35: mud turtle + 36: terrapin + 37: box turtle + 38: banded gecko + 39: green iguana + 40: Carolina anole + 41: desert grassland whiptail lizard + 42: agama + 43: frilled-necked lizard + 44: alligator lizard + 45: Gila monster + 46: European green lizard + 47: chameleon + 48: Komodo dragon + 49: Nile crocodile + 50: American alligator + 51: triceratops + 52: worm snake + 53: ring-necked snake + 54: eastern hog-nosed snake + 55: smooth green snake + 56: kingsnake + 57: garter snake + 58: water snake + 59: vine snake + 60: night snake + 61: boa constrictor + 62: African rock python + 63: Indian cobra + 64: green mamba + 65: sea snake + 66: Saharan horned viper + 67: eastern diamondback rattlesnake + 68: sidewinder + 69: trilobite + 70: harvestman + 71: scorpion + 72: yellow garden spider + 73: barn spider + 74: European garden spider + 75: southern black widow + 76: tarantula + 77: wolf spider + 78: tick + 79: centipede + 80: black grouse + 81: ptarmigan + 82: ruffed grouse + 83: prairie grouse + 84: peacock + 85: quail + 86: partridge + 87: grey parrot + 88: macaw + 89: sulphur-crested cockatoo + 90: lorikeet + 91: coucal + 92: bee eater + 93: hornbill + 94: hummingbird + 95: jacamar + 96: toucan + 97: duck + 98: red-breasted merganser + 99: goose + 100: black swan + 101: tusker + 102: echidna + 103: platypus + 104: wallaby + 105: koala + 106: wombat + 107: jellyfish + 108: sea anemone + 109: brain coral + 110: flatworm + 111: nematode + 112: conch + 113: snail + 114: slug + 115: sea slug + 116: chiton + 117: chambered nautilus + 118: Dungeness crab + 119: rock crab + 120: fiddler crab + 121: red king crab + 122: American lobster + 123: spiny lobster + 124: crayfish + 125: hermit crab + 126: isopod + 127: white stork + 128: black stork + 129: spoonbill + 130: flamingo + 131: little blue heron + 132: great egret + 133: bittern + 134: crane (bird) + 135: limpkin + 136: common gallinule + 137: American coot + 138: bustard + 139: ruddy turnstone + 140: dunlin + 141: common redshank + 142: dowitcher + 143: oystercatcher + 144: pelican + 145: king penguin + 146: albatross + 147: grey whale + 148: killer whale + 149: dugong + 150: sea lion + 151: Chihuahua + 152: Japanese Chin + 153: Maltese + 154: Pekingese + 155: Shih Tzu + 156: King Charles Spaniel + 157: Papillon + 158: toy terrier + 159: Rhodesian Ridgeback + 160: Afghan Hound + 161: Basset Hound + 162: Beagle + 163: Bloodhound + 164: Bluetick Coonhound + 165: Black and Tan Coonhound + 166: Treeing Walker Coonhound + 167: English foxhound + 168: Redbone Coonhound + 169: borzoi + 170: Irish Wolfhound + 171: Italian Greyhound + 172: Whippet + 173: Ibizan Hound + 174: Norwegian Elkhound + 175: Otterhound + 176: Saluki + 177: Scottish Deerhound + 178: Weimaraner + 179: Staffordshire Bull Terrier + 180: American Staffordshire Terrier + 181: Bedlington Terrier + 182: Border Terrier + 183: Kerry Blue Terrier + 184: Irish Terrier + 185: Norfolk Terrier + 186: Norwich Terrier + 187: Yorkshire Terrier + 188: Wire Fox Terrier + 189: Lakeland Terrier + 190: Sealyham Terrier + 191: Airedale Terrier + 192: Cairn Terrier + 193: Australian Terrier + 194: Dandie Dinmont Terrier + 195: Boston Terrier + 196: Miniature Schnauzer + 197: Giant Schnauzer + 198: Standard Schnauzer + 199: Scottish Terrier + 200: Tibetan Terrier + 201: Australian Silky Terrier + 202: Soft-coated Wheaten Terrier + 203: West Highland White Terrier + 204: Lhasa Apso + 205: Flat-Coated Retriever + 206: Curly-coated Retriever + 207: Golden Retriever + 208: Labrador Retriever + 209: Chesapeake Bay Retriever + 210: German Shorthaired Pointer + 211: Vizsla + 212: English Setter + 213: Irish Setter + 214: Gordon Setter + 215: Brittany + 216: Clumber Spaniel + 217: English Springer Spaniel + 218: Welsh Springer Spaniel + 219: Cocker Spaniels + 220: Sussex Spaniel + 221: Irish Water Spaniel + 222: Kuvasz + 223: Schipperke + 224: Groenendael + 225: Malinois + 226: Briard + 227: Australian Kelpie + 228: Komondor + 229: Old English Sheepdog + 230: Shetland Sheepdog + 231: collie + 232: Border Collie + 233: Bouvier des Flandres + 234: Rottweiler + 235: German Shepherd Dog + 236: Dobermann + 237: Miniature Pinscher + 238: Greater Swiss Mountain Dog + 239: Bernese Mountain Dog + 240: Appenzeller Sennenhund + 241: Entlebucher Sennenhund + 242: Boxer + 243: Bullmastiff + 244: Tibetan Mastiff + 245: French Bulldog + 246: Great Dane + 247: St. Bernard + 248: husky + 249: Alaskan Malamute + 250: Siberian Husky + 251: Dalmatian + 252: Affenpinscher + 253: Basenji + 254: pug + 255: Leonberger + 256: Newfoundland + 257: Pyrenean Mountain Dog + 258: Samoyed + 259: Pomeranian + 260: Chow Chow + 261: Keeshond + 262: Griffon Bruxellois + 263: Pembroke Welsh Corgi + 264: Cardigan Welsh Corgi + 265: Toy Poodle + 266: Miniature Poodle + 267: Standard Poodle + 268: Mexican hairless dog + 269: grey wolf + 270: Alaskan tundra wolf + 271: red wolf + 272: coyote + 273: dingo + 274: dhole + 275: African wild dog + 276: hyena + 277: red fox + 278: kit fox + 279: Arctic fox + 280: grey fox + 281: tabby cat + 282: tiger cat + 283: Persian cat + 284: Siamese cat + 285: Egyptian Mau + 286: cougar + 287: lynx + 288: leopard + 289: snow leopard + 290: jaguar + 291: lion + 292: tiger + 293: cheetah + 294: brown bear + 295: American black bear + 296: polar bear + 297: sloth bear + 298: mongoose + 299: meerkat + 300: tiger beetle + 301: ladybug + 302: ground beetle + 303: longhorn beetle + 304: leaf beetle + 305: dung beetle + 306: rhinoceros beetle + 307: weevil + 308: fly + 309: bee + 310: ant + 311: grasshopper + 312: cricket + 313: stick insect + 314: cockroach + 315: mantis + 316: cicada + 317: leafhopper + 318: lacewing + 319: dragonfly + 320: damselfly + 321: red admiral + 322: ringlet + 323: monarch butterfly + 324: small white + 325: sulphur butterfly + 326: gossamer-winged butterfly + 327: starfish + 328: sea urchin + 329: sea cucumber + 330: cottontail rabbit + 331: hare + 332: Angora rabbit + 333: hamster + 334: porcupine + 335: fox squirrel + 336: marmot + 337: beaver + 338: guinea pig + 339: common sorrel + 340: zebra + 341: pig + 342: wild boar + 343: warthog + 344: hippopotamus + 345: ox + 346: water buffalo + 347: bison + 348: ram + 349: bighorn sheep + 350: Alpine ibex + 351: hartebeest + 352: impala + 353: gazelle + 354: dromedary + 355: llama + 356: weasel + 357: mink + 358: European polecat + 359: black-footed ferret + 360: otter + 361: skunk + 362: badger + 363: armadillo + 364: three-toed sloth + 365: orangutan + 366: gorilla + 367: chimpanzee + 368: gibbon + 369: siamang + 370: guenon + 371: patas monkey + 372: baboon + 373: macaque + 374: langur + 375: black-and-white colobus + 376: proboscis monkey + 377: marmoset + 378: white-headed capuchin + 379: howler monkey + 380: titi + 381: Geoffroy's spider monkey + 382: common squirrel monkey + 383: ring-tailed lemur + 384: indri + 385: Asian elephant + 386: African bush elephant + 387: red panda + 388: giant panda + 389: snoek + 390: eel + 391: coho salmon + 392: rock beauty + 393: clownfish + 394: sturgeon + 395: garfish + 396: lionfish + 397: pufferfish + 398: abacus + 399: abaya + 400: academic gown + 401: accordion + 402: acoustic guitar + 403: aircraft carrier + 404: airliner + 405: airship + 406: altar + 407: ambulance + 408: amphibious vehicle + 409: analog clock + 410: apiary + 411: apron + 412: waste container + 413: assault rifle + 414: backpack + 415: bakery + 416: balance beam + 417: balloon + 418: ballpoint pen + 419: Band-Aid + 420: banjo + 421: baluster + 422: barbell + 423: barber chair + 424: barbershop + 425: barn + 426: barometer + 427: barrel + 428: wheelbarrow + 429: baseball + 430: basketball + 431: bassinet + 432: bassoon + 433: swimming cap + 434: bath towel + 435: bathtub + 436: station wagon + 437: lighthouse + 438: beaker + 439: military cap + 440: beer bottle + 441: beer glass + 442: bell-cot + 443: bib + 444: tandem bicycle + 445: bikini + 446: ring binder + 447: binoculars + 448: birdhouse + 449: boathouse + 450: bobsleigh + 451: bolo tie + 452: poke bonnet + 453: bookcase + 454: bookstore + 455: bottle cap + 456: bow + 457: bow tie + 458: brass + 459: bra + 460: breakwater + 461: breastplate + 462: broom + 463: bucket + 464: buckle + 465: bulletproof vest + 466: high-speed train + 467: butcher shop + 468: taxicab + 469: cauldron + 470: candle + 471: cannon + 472: canoe + 473: can opener + 474: cardigan + 475: car mirror + 476: carousel + 477: tool kit + 478: carton + 479: car wheel + 480: automated teller machine + 481: cassette + 482: cassette player + 483: castle + 484: catamaran + 485: CD player + 486: cello + 487: mobile phone + 488: chain + 489: chain-link fence + 490: chain mail + 491: chainsaw + 492: chest + 493: chiffonier + 494: chime + 495: china cabinet + 496: Christmas stocking + 497: church + 498: movie theater + 499: cleaver + 500: cliff dwelling + 501: cloak + 502: clogs + 503: cocktail shaker + 504: coffee mug + 505: coffeemaker + 506: coil + 507: combination lock + 508: computer keyboard + 509: confectionery store + 510: container ship + 511: convertible + 512: corkscrew + 513: cornet + 514: cowboy boot + 515: cowboy hat + 516: cradle + 517: crane (machine) + 518: crash helmet + 519: crate + 520: infant bed + 521: Crock Pot + 522: croquet ball + 523: crutch + 524: cuirass + 525: dam + 526: desk + 527: desktop computer + 528: rotary dial telephone + 529: diaper + 530: digital clock + 531: digital watch + 532: dining table + 533: dishcloth + 534: dishwasher + 535: disc brake + 536: dock + 537: dog sled + 538: dome + 539: doormat + 540: drilling rig + 541: drum + 542: drumstick + 543: dumbbell + 544: Dutch oven + 545: electric fan + 546: electric guitar + 547: electric locomotive + 548: entertainment center + 549: envelope + 550: espresso machine + 551: face powder + 552: feather boa + 553: filing cabinet + 554: fireboat + 555: fire engine + 556: fire screen sheet + 557: flagpole + 558: flute + 559: folding chair + 560: football helmet + 561: forklift + 562: fountain + 563: fountain pen + 564: four-poster bed + 565: freight car + 566: French horn + 567: frying pan + 568: fur coat + 569: garbage truck + 570: gas mask + 571: gas pump + 572: goblet + 573: go-kart + 574: golf ball + 575: golf cart + 576: gondola + 577: gong + 578: gown + 579: grand piano + 580: greenhouse + 581: grille + 582: grocery store + 583: guillotine + 584: barrette + 585: hair spray + 586: half-track + 587: hammer + 588: hamper + 589: hair dryer + 590: hand-held computer + 591: handkerchief + 592: hard disk drive + 593: harmonica + 594: harp + 595: harvester + 596: hatchet + 597: holster + 598: home theater + 599: honeycomb + 600: hook + 601: hoop skirt + 602: horizontal bar + 603: horse-drawn vehicle + 604: hourglass + 605: iPod + 606: clothes iron + 607: jack-o'-lantern + 608: jeans + 609: jeep + 610: T-shirt + 611: jigsaw puzzle + 612: pulled rickshaw + 613: joystick + 614: kimono + 615: knee pad + 616: knot + 617: lab coat + 618: ladle + 619: lampshade + 620: laptop computer + 621: lawn mower + 622: lens cap + 623: paper knife + 624: library + 625: lifeboat + 626: lighter + 627: limousine + 628: ocean liner + 629: lipstick + 630: slip-on shoe + 631: lotion + 632: speaker + 633: loupe + 634: sawmill + 635: magnetic compass + 636: mail bag + 637: mailbox + 638: tights + 639: tank suit + 640: manhole cover + 641: maraca + 642: marimba + 643: mask + 644: match + 645: maypole + 646: maze + 647: measuring cup + 648: medicine chest + 649: megalith + 650: microphone + 651: microwave oven + 652: military uniform + 653: milk can + 654: minibus + 655: miniskirt + 656: minivan + 657: missile + 658: mitten + 659: mixing bowl + 660: mobile home + 661: Model T + 662: modem + 663: monastery + 664: monitor + 665: moped + 666: mortar + 667: square academic cap + 668: mosque + 669: mosquito net + 670: scooter + 671: mountain bike + 672: tent + 673: computer mouse + 674: mousetrap + 675: moving van + 676: muzzle + 677: nail + 678: neck brace + 679: necklace + 680: nipple + 681: notebook computer + 682: obelisk + 683: oboe + 684: ocarina + 685: odometer + 686: oil filter + 687: organ + 688: oscilloscope + 689: overskirt + 690: bullock cart + 691: oxygen mask + 692: packet + 693: paddle + 694: paddle wheel + 695: padlock + 696: paintbrush + 697: pajamas + 698: palace + 699: pan flute + 700: paper towel + 701: parachute + 702: parallel bars + 703: park bench + 704: parking meter + 705: passenger car + 706: patio + 707: payphone + 708: pedestal + 709: pencil case + 710: pencil sharpener + 711: perfume + 712: Petri dish + 713: photocopier + 714: plectrum + 715: Pickelhaube + 716: picket fence + 717: pickup truck + 718: pier + 719: piggy bank + 720: pill bottle + 721: pillow + 722: ping-pong ball + 723: pinwheel + 724: pirate ship + 725: pitcher + 726: hand plane + 727: planetarium + 728: plastic bag + 729: plate rack + 730: plow + 731: plunger + 732: Polaroid camera + 733: pole + 734: police van + 735: poncho + 736: billiard table + 737: soda bottle + 738: pot + 739: potter's wheel + 740: power drill + 741: prayer rug + 742: printer + 743: prison + 744: projectile + 745: projector + 746: hockey puck + 747: punching bag + 748: purse + 749: quill + 750: quilt + 751: race car + 752: racket + 753: radiator + 754: radio + 755: radio telescope + 756: rain barrel + 757: recreational vehicle + 758: reel + 759: reflex camera + 760: refrigerator + 761: remote control + 762: restaurant + 763: revolver + 764: rifle + 765: rocking chair + 766: rotisserie + 767: eraser + 768: rugby ball + 769: ruler + 770: running shoe + 771: safe + 772: safety pin + 773: salt shaker + 774: sandal + 775: sarong + 776: saxophone + 777: scabbard + 778: weighing scale + 779: school bus + 780: schooner + 781: scoreboard + 782: CRT screen + 783: screw + 784: screwdriver + 785: seat belt + 786: sewing machine + 787: shield + 788: shoe store + 789: shoji + 790: shopping basket + 791: shopping cart + 792: shovel + 793: shower cap + 794: shower curtain + 795: ski + 796: ski mask + 797: sleeping bag + 798: slide rule + 799: sliding door + 800: slot machine + 801: snorkel + 802: snowmobile + 803: snowplow + 804: soap dispenser + 805: soccer ball + 806: sock + 807: solar thermal collector + 808: sombrero + 809: soup bowl + 810: space bar + 811: space heater + 812: space shuttle + 813: spatula + 814: motorboat + 815: spider web + 816: spindle + 817: sports car + 818: spotlight + 819: stage + 820: steam locomotive + 821: through arch bridge + 822: steel drum + 823: stethoscope + 824: scarf + 825: stone wall + 826: stopwatch + 827: stove + 828: strainer + 829: tram + 830: stretcher + 831: couch + 832: stupa + 833: submarine + 834: suit + 835: sundial + 836: sunglass + 837: sunglasses + 838: sunscreen + 839: suspension bridge + 840: mop + 841: sweatshirt + 842: swimsuit + 843: swing + 844: switch + 845: syringe + 846: table lamp + 847: tank + 848: tape player + 849: teapot + 850: teddy bear + 851: television + 852: tennis ball + 853: thatched roof + 854: front curtain + 855: thimble + 856: threshing machine + 857: throne + 858: tile roof + 859: toaster + 860: tobacco shop + 861: toilet seat + 862: torch + 863: totem pole + 864: tow truck + 865: toy store + 866: tractor + 867: semi-trailer truck + 868: tray + 869: trench coat + 870: tricycle + 871: trimaran + 872: tripod + 873: triumphal arch + 874: trolleybus + 875: trombone + 876: tub + 877: turnstile + 878: typewriter keyboard + 879: umbrella + 880: unicycle + 881: upright piano + 882: vacuum cleaner + 883: vase + 884: vault + 885: velvet + 886: vending machine + 887: vestment + 888: viaduct + 889: violin + 890: volleyball + 891: waffle iron + 892: wall clock + 893: wallet + 894: wardrobe + 895: military aircraft + 896: sink + 897: washing machine + 898: water bottle + 899: water jug + 900: water tower + 901: whiskey jug + 902: whistle + 903: wig + 904: window screen + 905: window shade + 906: Windsor tie + 907: wine bottle + 908: wing + 909: wok + 910: wooden spoon + 911: wool + 912: split-rail fence + 913: shipwreck + 914: yawl + 915: yurt + 916: website + 917: comic book + 918: crossword + 919: traffic sign + 920: traffic light + 921: dust jacket + 922: menu + 923: plate + 924: guacamole + 925: consomme + 926: hot pot + 927: trifle + 928: ice cream + 929: ice pop + 930: baguette + 931: bagel + 932: pretzel + 933: cheeseburger + 934: hot dog + 935: mashed potato + 936: cabbage + 937: broccoli + 938: cauliflower + 939: zucchini + 940: spaghetti squash + 941: acorn squash + 942: butternut squash + 943: cucumber + 944: artichoke + 945: bell pepper + 946: cardoon + 947: mushroom + 948: Granny Smith + 949: strawberry + 950: orange + 951: lemon + 952: fig + 953: pineapple + 954: banana + 955: jackfruit + 956: custard apple + 957: pomegranate + 958: hay + 959: carbonara + 960: chocolate syrup + 961: dough + 962: meatloaf + 963: pizza + 964: pot pie + 965: burrito + 966: red wine + 967: espresso + 968: cup + 969: eggnog + 970: alp + 971: bubble + 972: cliff + 973: coral reef + 974: geyser + 975: lakeshore + 976: promontory + 977: shoal + 978: seashore + 979: valley + 980: volcano + 981: baseball player + 982: bridegroom + 983: scuba diver + 984: rapeseed + 985: daisy + 986: yellow lady's slipper + 987: corn + 988: acorn + 989: rose hip + 990: horse chestnut seed + 991: coral fungus + 992: agaric + 993: gyromitra + 994: stinkhorn mushroom + 995: earth star + 996: hen-of-the-woods + 997: bolete + 998: ear + 999: toilet paper + +# Download script/URL (optional) +download: data/scripts/get_imagenet1000.sh diff --git a/yolov5/data/Objects365.yaml b/yolov5/data/Objects365.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f1f0a1ae489135d204208d7dc5928a172e550385 --- /dev/null +++ b/yolov5/data/Objects365.yaml @@ -0,0 +1,436 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# Objects365 dataset https://www.objects365.org/ by Megvii +# Example usage: python train.py --data Objects365.yaml +# parent +# ├── yolov5 +# └── datasets +# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips) + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/Objects365 # dataset root dir +train: images/train # train images (relative to 'path') 1742289 images +val: images/val # val images (relative to 'path') 80000 images +test: # test images (optional) + +# Classes +names: + 0: Person + 1: Sneakers + 2: Chair + 3: Other Shoes + 4: Hat + 5: Car + 6: Lamp + 7: Glasses + 8: Bottle + 9: Desk + 10: Cup + 11: Street Lights + 12: Cabinet/shelf + 13: Handbag/Satchel + 14: Bracelet + 15: Plate + 16: Picture/Frame + 17: Helmet + 18: Book + 19: Gloves + 20: Storage box + 21: Boat + 22: Leather Shoes + 23: Flower + 24: Bench + 25: Potted Plant + 26: Bowl/Basin + 27: Flag + 28: Pillow + 29: Boots + 30: Vase + 31: Microphone + 32: Necklace + 33: Ring + 34: SUV + 35: Wine Glass + 36: Belt + 37: Monitor/TV + 38: Backpack + 39: Umbrella + 40: Traffic Light + 41: Speaker + 42: Watch + 43: Tie + 44: Trash bin Can + 45: Slippers + 46: Bicycle + 47: Stool + 48: Barrel/bucket + 49: Van + 50: Couch + 51: Sandals + 52: Basket + 53: Drum + 54: Pen/Pencil + 55: Bus + 56: Wild Bird + 57: High Heels + 58: Motorcycle + 59: Guitar + 60: Carpet + 61: Cell Phone + 62: Bread + 63: Camera + 64: Canned + 65: Truck + 66: Traffic cone + 67: Cymbal + 68: Lifesaver + 69: Towel + 70: Stuffed Toy + 71: Candle + 72: Sailboat + 73: Laptop + 74: Awning + 75: Bed + 76: Faucet + 77: Tent + 78: Horse + 79: Mirror + 80: Power outlet + 81: Sink + 82: Apple + 83: Air Conditioner + 84: Knife + 85: Hockey Stick + 86: Paddle + 87: Pickup Truck + 88: Fork + 89: Traffic Sign + 90: Balloon + 91: Tripod + 92: Dog + 93: Spoon + 94: Clock + 95: Pot + 96: Cow + 97: Cake + 98: Dinning Table + 99: Sheep + 100: Hanger + 101: Blackboard/Whiteboard + 102: Napkin + 103: Other Fish + 104: Orange/Tangerine + 105: Toiletry + 106: Keyboard + 107: Tomato + 108: Lantern + 109: Machinery Vehicle + 110: Fan + 111: Green Vegetables + 112: Banana + 113: Baseball Glove + 114: Airplane + 115: Mouse + 116: Train + 117: Pumpkin + 118: Soccer + 119: Skiboard + 120: Luggage + 121: Nightstand + 122: Tea pot + 123: Telephone + 124: Trolley + 125: Head Phone + 126: Sports Car + 127: Stop Sign + 128: Dessert + 129: Scooter + 130: Stroller + 131: Crane + 132: Remote + 133: Refrigerator + 134: Oven + 135: Lemon + 136: Duck + 137: Baseball Bat + 138: Surveillance Camera + 139: Cat + 140: Jug + 141: Broccoli + 142: Piano + 143: Pizza + 144: Elephant + 145: Skateboard + 146: Surfboard + 147: Gun + 148: Skating and Skiing shoes + 149: Gas stove + 150: Donut + 151: Bow Tie + 152: Carrot + 153: Toilet + 154: Kite + 155: Strawberry + 156: Other Balls + 157: Shovel + 158: Pepper + 159: Computer Box + 160: Toilet Paper + 161: Cleaning Products + 162: Chopsticks + 163: Microwave + 164: Pigeon + 165: Baseball + 166: Cutting/chopping Board + 167: Coffee Table + 168: Side Table + 169: Scissors + 170: Marker + 171: Pie + 172: Ladder + 173: Snowboard + 174: Cookies + 175: Radiator + 176: Fire Hydrant + 177: Basketball + 178: Zebra + 179: Grape + 180: Giraffe + 181: Potato + 182: Sausage + 183: Tricycle + 184: Violin + 185: Egg + 186: Fire Extinguisher + 187: Candy + 188: Fire Truck + 189: Billiards + 190: Converter + 191: Bathtub + 192: Wheelchair + 193: Golf Club + 194: Briefcase + 195: Cucumber + 196: Cigar/Cigarette + 197: Paint Brush + 198: Pear + 199: Heavy Truck + 200: Hamburger + 201: Extractor + 202: Extension Cord + 203: Tong + 204: Tennis Racket + 205: Folder + 206: American Football + 207: earphone + 208: Mask + 209: Kettle + 210: Tennis + 211: Ship + 212: Swing + 213: Coffee Machine + 214: Slide + 215: Carriage + 216: Onion + 217: Green beans + 218: Projector + 219: Frisbee + 220: Washing Machine/Drying Machine + 221: Chicken + 222: Printer + 223: Watermelon + 224: Saxophone + 225: Tissue + 226: Toothbrush + 227: Ice cream + 228: Hot-air balloon + 229: Cello + 230: French Fries + 231: Scale + 232: Trophy + 233: Cabbage + 234: Hot dog + 235: Blender + 236: Peach + 237: Rice + 238: Wallet/Purse + 239: Volleyball + 240: Deer + 241: Goose + 242: Tape + 243: Tablet + 244: Cosmetics + 245: Trumpet + 246: Pineapple + 247: Golf Ball + 248: Ambulance + 249: Parking meter + 250: Mango + 251: Key + 252: Hurdle + 253: Fishing Rod + 254: Medal + 255: Flute + 256: Brush + 257: Penguin + 258: Megaphone + 259: Corn + 260: Lettuce + 261: Garlic + 262: Swan + 263: Helicopter + 264: Green Onion + 265: Sandwich + 266: Nuts + 267: Speed Limit Sign + 268: Induction Cooker + 269: Broom + 270: Trombone + 271: Plum + 272: Rickshaw + 273: Goldfish + 274: Kiwi fruit + 275: Router/modem + 276: Poker Card + 277: Toaster + 278: Shrimp + 279: Sushi + 280: Cheese + 281: Notepaper + 282: Cherry + 283: Pliers + 284: CD + 285: Pasta + 286: Hammer + 287: Cue + 288: Avocado + 289: Hamimelon + 290: Flask + 291: Mushroom + 292: Screwdriver + 293: Soap + 294: Recorder + 295: Bear + 296: Eggplant + 297: Board Eraser + 298: Coconut + 299: Tape Measure/Ruler + 300: Pig + 301: Showerhead + 302: Globe + 303: Chips + 304: Steak + 305: Crosswalk Sign + 306: Stapler + 307: Camel + 308: Formula 1 + 309: Pomegranate + 310: Dishwasher + 311: Crab + 312: Hoverboard + 313: Meat ball + 314: Rice Cooker + 315: Tuba + 316: Calculator + 317: Papaya + 318: Antelope + 319: Parrot + 320: Seal + 321: Butterfly + 322: Dumbbell + 323: Donkey + 324: Lion + 325: Urinal + 326: Dolphin + 327: Electric Drill + 328: Hair Dryer + 329: Egg tart + 330: Jellyfish + 331: Treadmill + 332: Lighter + 333: Grapefruit + 334: Game board + 335: Mop + 336: Radish + 337: Baozi + 338: Target + 339: French + 340: Spring Rolls + 341: Monkey + 342: Rabbit + 343: Pencil Case + 344: Yak + 345: Red Cabbage + 346: Binoculars + 347: Asparagus + 348: Barbell + 349: Scallop + 350: Noddles + 351: Comb + 352: Dumpling + 353: Oyster + 354: Table Tennis paddle + 355: Cosmetics Brush/Eyeliner Pencil + 356: Chainsaw + 357: Eraser + 358: Lobster + 359: Durian + 360: Okra + 361: Lipstick + 362: Cosmetics Mirror + 363: Curling + 364: Table Tennis + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + from tqdm import tqdm + + from utils.general import Path, check_requirements, download, np, xyxy2xywhn + + check_requirements('pycocotools>=2.0') + from pycocotools.coco import COCO + + # Make Directories + dir = Path(yaml['path']) # dataset root dir + for p in 'images', 'labels': + (dir / p).mkdir(parents=True, exist_ok=True) + for q in 'train', 'val': + (dir / p / q).mkdir(parents=True, exist_ok=True) + + # Train, Val Splits + for split, patches in [('train', 50 + 1), ('val', 43 + 1)]: + print(f"Processing {split} in {patches} patches ...") + images, labels = dir / 'images' / split, dir / 'labels' / split + + # Download + url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/" + if split == 'train': + download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False) # annotations json + download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8) + elif split == 'val': + download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False) # annotations json + download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8) + download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8) + + # Move + for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'): + f.rename(images / f.name) # move to /images/{split} + + # Labels + coco = COCO(dir / f'zhiyuan_objv2_{split}.json') + names = [x["name"] for x in coco.loadCats(coco.getCatIds())] + for cid, cat in enumerate(names): + catIds = coco.getCatIds(catNms=[cat]) + imgIds = coco.getImgIds(catIds=catIds) + for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'): + width, height = im["width"], im["height"] + path = Path(im["file_name"]) # image filename + try: + with open(labels / path.with_suffix('.txt').name, 'a') as file: + annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=False) + for a in coco.loadAnns(annIds): + x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner) + xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4) + x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped + file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n") + except Exception as e: + print(e) diff --git a/yolov5/data/SKU-110K.yaml b/yolov5/data/SKU-110K.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b012bec3128b57778875d557c2e2a81ee10bfd5e --- /dev/null +++ b/yolov5/data/SKU-110K.yaml @@ -0,0 +1,51 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail +# Example usage: python train.py --data SKU-110K.yaml +# parent +# ├── yolov5 +# └── datasets +# └── SKU-110K ← downloads here (13.6 GB) + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/SKU-110K # dataset root dir +train: train.txt # train images (relative to 'path') 8219 images +val: val.txt # val images (relative to 'path') 588 images +test: test.txt # test images (optional) 2936 images + +# Classes +names: + 0: object + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + import shutil + from tqdm import tqdm + from utils.general import np, pd, Path, download, xyxy2xywh + + + # Download + dir = Path(yaml['path']) # dataset root dir + parent = Path(dir.parent) # download dir + urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz'] + download(urls, dir=parent, delete=False) + + # Rename directories + if dir.exists(): + shutil.rmtree(dir) + (parent / 'SKU110K_fixed').rename(dir) # rename dir + (dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir + + # Convert labels + names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names + for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv': + x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations + images, unique_images = x[:, 0], np.unique(x[:, 0]) + with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f: + f.writelines(f'./images/{s}\n' for s in unique_images) + for im in tqdm(unique_images, desc=f'Converting {dir / d}'): + cls = 0 # single-class dataset + with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f: + for r in x[images == im]: + w, h = r[6], r[7] # image width, height + xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance + f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label diff --git a/yolov5/data/VOC.yaml b/yolov5/data/VOC.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6929f015dcbf2561fd2463ade48540d4180851b1 --- /dev/null +++ b/yolov5/data/VOC.yaml @@ -0,0 +1,98 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford +# Example usage: python train.py --data VOC.yaml +# parent +# ├── yolov5 +# └── datasets +# └── VOC ← downloads here (2.8 GB) + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/VOC +train: # train images (relative to 'path') 16551 images + - images/train2012 + - images/train2007 + - images/val2012 + - images/val2007 +val: # val images (relative to 'path') 4952 images + - images/test2007 +test: # test images (optional) + - images/test2007 + +# Classes +names: + 0: aeroplane + 1: bicycle + 2: bird + 3: boat + 4: bottle + 5: bus + 6: car + 7: cat + 8: chair + 9: cow + 10: diningtable + 11: dog + 12: horse + 13: motorbike + 14: person + 15: pottedplant + 16: sheep + 17: sofa + 18: train + 19: tvmonitor + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + import xml.etree.ElementTree as ET + + from tqdm import tqdm + from utils.general import download, Path + + + def convert_label(path, lb_path, year, image_id): + def convert_box(size, box): + dw, dh = 1. / size[0], 1. / size[1] + x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2] + return x * dw, y * dh, w * dw, h * dh + + in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml') + out_file = open(lb_path, 'w') + tree = ET.parse(in_file) + root = tree.getroot() + size = root.find('size') + w = int(size.find('width').text) + h = int(size.find('height').text) + + names = list(yaml['names'].values()) # names list + for obj in root.iter('object'): + cls = obj.find('name').text + if cls in names and int(obj.find('difficult').text) != 1: + xmlbox = obj.find('bndbox') + bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')]) + cls_id = names.index(cls) # class id + out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n') + + + # Download + dir = Path(yaml['path']) # dataset root dir + url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' + urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images + f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images + f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images + download(urls, dir=dir / 'images', delete=False, curl=True, threads=3) + + # Convert + path = dir / 'images/VOCdevkit' + for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'): + imgs_path = dir / 'images' / f'{image_set}{year}' + lbs_path = dir / 'labels' / f'{image_set}{year}' + imgs_path.mkdir(exist_ok=True, parents=True) + lbs_path.mkdir(exist_ok=True, parents=True) + + with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f: + image_ids = f.read().strip().split() + for id in tqdm(image_ids, desc=f'{image_set}{year}'): + f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path + lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path + f.rename(imgs_path / f.name) # move image + convert_label(path, lb_path, year, id) # convert labels to YOLO format diff --git a/yolov5/data/VisDrone.yaml b/yolov5/data/VisDrone.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1dc4e3e0d054acbaf05c8454fd4752ae104411b3 --- /dev/null +++ b/yolov5/data/VisDrone.yaml @@ -0,0 +1,68 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University +# Example usage: python train.py --data VisDrone.yaml +# parent +# ├── yolov5 +# └── datasets +# └── VisDrone ← downloads here (2.3 GB) + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/VisDrone # dataset root dir +train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images +val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images +test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images + +# Classes +names: + 0: pedestrian + 1: people + 2: bicycle + 3: car + 4: van + 5: truck + 6: tricycle + 7: awning-tricycle + 8: bus + 9: motor + +# Download script/URL (optional) --------------------------------------------------------------------------------------- +download: | + from utils.general import download, os, Path + + def visdrone2yolo(dir): + from PIL import Image + from tqdm import tqdm + + def convert_box(size, box): + # Convert VisDrone box to YOLO xywh box + dw = 1. / size[0] + dh = 1. / size[1] + return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh + + (dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory + pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}') + for f in pbar: + img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size + lines = [] + with open(f, 'r') as file: # read annotation.txt + for row in [x.split(',') for x in file.read().strip().splitlines()]: + if row[4] == '0': # VisDrone 'ignored regions' class 0 + continue + cls = int(row[5]) - 1 + box = convert_box(img_size, tuple(map(int, row[:4]))) + lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n") + with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl: + fl.writelines(lines) # write label.txt + + + # Download + dir = Path(yaml['path']) # dataset root dir + urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip', + 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip', + 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip', + 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip'] + download(urls, dir=dir, curl=True, threads=4) + + # Convert + for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev': + visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels diff --git a/yolov5/data/coco.yaml b/yolov5/data/coco.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4bfd52a9d5367f79ca7c0fb929d674f54a589b91 --- /dev/null +++ b/yolov5/data/coco.yaml @@ -0,0 +1,114 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# COCO 2017 dataset http://cocodataset.org by Microsoft +# Example usage: python train.py --data coco.yaml +# parent +# ├── yolov5 +# └── datasets +# └── coco ← downloads here (20.1 GB) + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/coco # dataset root dir +train: train2017.txt # train images (relative to 'path') 118287 images +val: val2017.txt # val images (relative to 'path') 5000 images +test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 + +# Classes +names: + 0: person + 1: bicycle + 2: car + 3: motorcycle + 4: airplane + 5: bus + 6: train + 7: truck + 8: boat + 9: traffic light + 10: fire hydrant + 11: stop sign + 12: parking meter + 13: bench + 14: bird + 15: cat + 16: dog + 17: horse + 18: sheep + 19: cow + 20: elephant + 21: bear + 22: zebra + 23: giraffe + 24: backpack + 25: umbrella + 26: handbag + 27: tie + 28: suitcase + 29: frisbee + 30: skis + 31: snowboard + 32: sports ball + 33: kite + 34: baseball bat + 35: baseball glove + 36: skateboard + 37: surfboard + 38: tennis racket + 39: bottle + 40: wine glass + 41: cup + 42: fork + 43: knife + 44: spoon + 45: bowl + 46: banana + 47: apple + 48: sandwich + 49: orange + 50: broccoli + 51: carrot + 52: hot dog + 53: pizza + 54: donut + 55: cake + 56: chair + 57: couch + 58: potted plant + 59: bed + 60: dining table + 61: toilet + 62: tv + 63: laptop + 64: mouse + 65: remote + 66: keyboard + 67: cell phone + 68: microwave + 69: oven + 70: toaster + 71: sink + 72: refrigerator + 73: book + 74: clock + 75: vase + 76: scissors + 77: teddy bear + 78: hair drier + 79: toothbrush + +# Download script/URL (optional) +download: | + from utils.general import download, Path + + + # Download labels + segments = False # segment or box labels + dir = Path(yaml['path']) # dataset root dir + url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' + urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels + download(urls, dir=dir.parent) + + # Download data + urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images + 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images + 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional) + download(urls, dir=dir / 'images', threads=3) diff --git a/yolov5/data/coco128-seg.yaml b/yolov5/data/coco128-seg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a96ee8ff66966dd58feb1335ca583857c836da9d --- /dev/null +++ b/yolov5/data/coco128-seg.yaml @@ -0,0 +1,99 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics +# Example usage: python train.py --data coco128.yaml +# parent +# ├── yolov5 +# └── datasets +# └── coco128-seg ← downloads here (7 MB) + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/coco128-seg # dataset root dir +train: images/train2017 # train images (relative to 'path') 128 images +val: images/train2017 # val images (relative to 'path') 128 images +test: # test images (optional) + +# Classes +names: + 0: person + 1: bicycle + 2: car + 3: motorcycle + 4: airplane + 5: bus + 6: train + 7: truck + 8: boat + 9: traffic light + 10: fire hydrant + 11: stop sign + 12: parking meter + 13: bench + 14: bird + 15: cat + 16: dog + 17: horse + 18: sheep + 19: cow + 20: elephant + 21: bear + 22: zebra + 23: giraffe + 24: backpack + 25: umbrella + 26: handbag + 27: tie + 28: suitcase + 29: frisbee + 30: skis + 31: snowboard + 32: sports ball + 33: kite + 34: baseball bat + 35: baseball glove + 36: skateboard + 37: surfboard + 38: tennis racket + 39: bottle + 40: wine glass + 41: cup + 42: fork + 43: knife + 44: spoon + 45: bowl + 46: banana + 47: apple + 48: sandwich + 49: orange + 50: broccoli + 51: carrot + 52: hot dog + 53: pizza + 54: donut + 55: cake + 56: chair + 57: couch + 58: potted plant + 59: bed + 60: dining table + 61: toilet + 62: tv + 63: laptop + 64: mouse + 65: remote + 66: keyboard + 67: cell phone + 68: microwave + 69: oven + 70: toaster + 71: sink + 72: refrigerator + 73: book + 74: clock + 75: vase + 76: scissors + 77: teddy bear + 78: hair drier + 79: toothbrush + +# Download script/URL (optional) +download: https://ultralytics.com/assets/coco128-seg.zip diff --git a/yolov5/data/coco128.yaml b/yolov5/data/coco128.yaml new file mode 100644 index 0000000000000000000000000000000000000000..074903dd0ddf8af3224f3ba2601f96d4c4b8c433 --- /dev/null +++ b/yolov5/data/coco128.yaml @@ -0,0 +1,99 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics +# Example usage: python train.py --data coco128.yaml +# parent +# ├── yolov5 +# └── datasets +# └── coco128 ← downloads here (7 MB) + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: ../datasets/coco128 # dataset root dir +train: images/train2017 # train images (relative to 'path') 128 images +val: images/train2017 # val images (relative to 'path') 128 images +test: # test images (optional) + +# Classes +names: + 0: person + 1: bicycle + 2: car + 3: motorcycle + 4: airplane + 5: bus + 6: train + 7: truck + 8: boat + 9: traffic light + 10: fire hydrant + 11: stop sign + 12: parking meter + 13: bench + 14: bird + 15: cat + 16: dog + 17: horse + 18: sheep + 19: cow + 20: elephant + 21: bear + 22: zebra + 23: giraffe + 24: backpack + 25: umbrella + 26: handbag + 27: tie + 28: suitcase + 29: frisbee + 30: skis + 31: snowboard + 32: sports ball + 33: kite + 34: baseball bat + 35: baseball glove + 36: skateboard + 37: surfboard + 38: tennis racket + 39: bottle + 40: wine glass + 41: cup + 42: fork + 43: knife + 44: spoon + 45: bowl + 46: banana + 47: apple + 48: sandwich + 49: orange + 50: broccoli + 51: carrot + 52: hot dog + 53: pizza + 54: donut + 55: cake + 56: chair + 57: couch + 58: potted plant + 59: bed + 60: dining table + 61: toilet + 62: tv + 63: laptop + 64: mouse + 65: remote + 66: keyboard + 67: cell phone + 68: microwave + 69: oven + 70: toaster + 71: sink + 72: refrigerator + 73: book + 74: clock + 75: vase + 76: scissors + 77: teddy bear + 78: hair drier + 79: toothbrush + +# Download script/URL (optional) +download: https://ultralytics.com/assets/coco128.zip diff --git a/yolov5/data/hyps/hyp.Objects365.yaml b/yolov5/data/hyps/hyp.Objects365.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7a6c507c73bf8f15817677e525ad75ffab328357 --- /dev/null +++ b/yolov5/data/hyps/hyp.Objects365.yaml @@ -0,0 +1,34 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# Hyperparameters for Objects365 training +# python train.py --weights yolov5m.pt --data Objects365.yaml --evolve +# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials + +lr0: 0.00258 +lrf: 0.17 +momentum: 0.779 +weight_decay: 0.00058 +warmup_epochs: 1.33 +warmup_momentum: 0.86 +warmup_bias_lr: 0.0711 +box: 0.0539 +cls: 0.299 +cls_pw: 0.825 +obj: 0.632 +obj_pw: 1.0 +iou_t: 0.2 +anchor_t: 3.44 +anchors: 3.2 +fl_gamma: 0.0 +hsv_h: 0.0188 +hsv_s: 0.704 +hsv_v: 0.36 +degrees: 0.0 +translate: 0.0902 +scale: 0.491 +shear: 0.0 +perspective: 0.0 +flipud: 0.0 +fliplr: 0.5 +mosaic: 1.0 +mixup: 0.0 +copy_paste: 0.0 diff --git a/yolov5/data/hyps/hyp.VOC.yaml b/yolov5/data/hyps/hyp.VOC.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c04c63e21ea0259d7e8fe834e95544caf52975da --- /dev/null +++ b/yolov5/data/hyps/hyp.VOC.yaml @@ -0,0 +1,40 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# Hyperparameters for VOC training +# python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve +# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials + +# YOLOv5 Hyperparameter Evolution Results +# Best generation: 467 +# Last generation: 996 +# metrics/precision, metrics/recall, metrics/mAP_0.5, metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss +# 0.87729, 0.85125, 0.91286, 0.72664, 0.0076739, 0.0042529, 0.0013865 + +lr0: 0.00334 +lrf: 0.15135 +momentum: 0.74832 +weight_decay: 0.00025 +warmup_epochs: 3.3835 +warmup_momentum: 0.59462 +warmup_bias_lr: 0.18657 +box: 0.02 +cls: 0.21638 +cls_pw: 0.5 +obj: 0.51728 +obj_pw: 0.67198 +iou_t: 0.2 +anchor_t: 3.3744 +fl_gamma: 0.0 +hsv_h: 0.01041 +hsv_s: 0.54703 +hsv_v: 0.27739 +degrees: 0.0 +translate: 0.04591 +scale: 0.75544 +shear: 0.0 +perspective: 0.0 +flipud: 0.0 +fliplr: 0.5 +mosaic: 0.85834 +mixup: 0.04266 +copy_paste: 0.0 +anchors: 3.412 diff --git a/yolov5/data/hyps/hyp.no-augmentation.yaml b/yolov5/data/hyps/hyp.no-augmentation.yaml new file mode 100644 index 0000000000000000000000000000000000000000..adc360bb83f854c099145c709110cd03ddcf326d --- /dev/null +++ b/yolov5/data/hyps/hyp.no-augmentation.yaml @@ -0,0 +1,35 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# Hyperparameters when using Albumentations frameworks +# python train.py --hyp hyp.no-augmentation.yaml +# See https://github.com/ultralytics/yolov5/pull/3882 for YOLOv5 + Albumentations Usage examples + +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +# this parameters are all zero since we want to use albumentation framework +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0 # image HSV-Hue augmentation (fraction) +hsv_s: 0 # image HSV-Saturation augmentation (fraction) +hsv_v: 0 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0 # image translation (+/- fraction) +scale: 0 # image scale (+/- gain) +shear: 0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.0 # image flip left-right (probability) +mosaic: 0.0 # image mosaic (probability) +mixup: 0.0 # image mixup (probability) +copy_paste: 0.0 # segment copy-paste (probability) diff --git a/yolov5/data/hyps/hyp.scratch-high.yaml b/yolov5/data/hyps/hyp.scratch-high.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3e913e36df1636973908d96e91e4b4ce832fa5cb --- /dev/null +++ b/yolov5/data/hyps/hyp.scratch-high.yaml @@ -0,0 +1,34 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# Hyperparameters for high-augmentation COCO training from scratch +# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300 +# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials + +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.1 # image translation (+/- fraction) +scale: 0.9 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.1 # image mixup (probability) +copy_paste: 0.1 # segment copy-paste (probability) diff --git a/yolov5/data/hyps/hyp.scratch-low.yaml b/yolov5/data/hyps/hyp.scratch-low.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ff0d1e7ff1acbd9b605f86a945860237f1863197 --- /dev/null +++ b/yolov5/data/hyps/hyp.scratch-low.yaml @@ -0,0 +1,34 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# Hyperparameters for low-augmentation COCO training from scratch +# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear +# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials + +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.5 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 1.0 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.1 # image translation (+/- fraction) +scale: 0.5 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.0 # image mixup (probability) +copy_paste: 0.0 # segment copy-paste (probability) diff --git a/yolov5/data/hyps/hyp.scratch-med.yaml b/yolov5/data/hyps/hyp.scratch-med.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c2fba1fc2b802f016cfc541163a039196d9a3c86 --- /dev/null +++ b/yolov5/data/hyps/hyp.scratch-med.yaml @@ -0,0 +1,34 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# Hyperparameters for medium-augmentation COCO training from scratch +# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300 +# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials + +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.3 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 0.7 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.1 # image translation (+/- fraction) +scale: 0.9 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.1 # image mixup (probability) +copy_paste: 0.0 # segment copy-paste (probability) diff --git a/yolov5/data/images/bus.jpg b/yolov5/data/images/bus.jpg new file mode 100644 index 0000000000000000000000000000000000000000..b43e311165c785f000eb7493ff8fb662d06a3f83 Binary files /dev/null and b/yolov5/data/images/bus.jpg differ diff --git a/yolov5/data/images/zidane.jpg b/yolov5/data/images/zidane.jpg new file mode 100644 index 0000000000000000000000000000000000000000..92d72ea124760ce5dbf9425e3aa8f371e7481328 Binary files /dev/null and b/yolov5/data/images/zidane.jpg differ diff --git a/yolov5/data/scripts/download_weights.sh b/yolov5/data/scripts/download_weights.sh new file mode 100644 index 0000000000000000000000000000000000000000..e408959b32b245f5a6bb1291db16afd138c56a37 --- /dev/null +++ b/yolov5/data/scripts/download_weights.sh @@ -0,0 +1,22 @@ +#!/bin/bash +# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license +# Download latest models from https://github.com/ultralytics/yolov5/releases +# Example usage: bash data/scripts/download_weights.sh +# parent +# └── yolov5 +# ├── yolov5s.pt ← downloads here +# ├── yolov5m.pt +# └── ... + +python - <= cls >= 0, f'incorrect class index {cls}' + + # Write YOLO label + if id not in shapes: + shapes[id] = Image.open(file).size + box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True) + with open((labels / id).with_suffix('.txt'), 'a') as f: + f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n") # write label.txt + except Exception as e: + print(f'WARNING: skipping one label for {file}: {e}') + + + # Download manually from https://challenge.xviewdataset.org + dir = Path(yaml['path']) # dataset root dir + # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip', # train labels + # 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images + # 'https://d307kc0mrhucc3.cloudfront.net/val_images.zip'] # 5G, 282 val images (no labels) + # download(urls, dir=dir, delete=False) + + # Convert labels + convert_labels(dir / 'xView_train.geojson') + + # Move images + images = Path(dir / 'images') + images.mkdir(parents=True, exist_ok=True) + Path(dir / 'train_images').rename(dir / 'images' / 'train') + Path(dir / 'val_images').rename(dir / 'images' / 'val') + + # Split + autosplit(dir / 'images' / 'train') diff --git a/yolov5/detect.py b/yolov5/detect.py new file mode 100644 index 0000000000000000000000000000000000000000..f791faa090879ce57240fa6ebb25ac0709ec7091 --- /dev/null +++ b/yolov5/detect.py @@ -0,0 +1,313 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +""" +Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc. + +Usage - sources: + $ python detect.py --weights yolov5s.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/LNwODJXcvt4' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream + +Usage - formats: + $ python detect.py --weights yolov5s.pt # PyTorch + yolov5s.torchscript # TorchScript + yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s_openvino_model # OpenVINO + yolov5s.engine # TensorRT + yolov5s.mlmodel # CoreML (macOS-only) + yolov5s_saved_model # TensorFlow SavedModel + yolov5s.pb # TensorFlow GraphDef + yolov5s.tflite # TensorFlow Lite + yolov5s_edgetpu.tflite # TensorFlow Edge TPU + yolov5s_paddle_model # PaddlePaddle +""" + +import argparse +import csv +import os +import platform +import sys +from pathlib import Path + +import torch + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from ultralytics.utils.plotting import Annotator, colors, save_one_box + +from models.common import DetectMultiBackend +from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams +from utils.general import ( + LOGGER, + Profile, + check_file, + check_img_size, + check_imshow, + check_requirements, + colorstr, + cv2, + increment_path, + non_max_suppression, + print_args, + scale_boxes, + strip_optimizer, + xyxy2xywh, +) +from utils.torch_utils import select_device, smart_inference_mode + + +@smart_inference_mode() +def run( + weights=ROOT / "yolov5s.pt", # model path or triton URL + source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam) + data=ROOT / "data/coco128.yaml", # dataset.yaml path + imgsz=(640, 640), # inference size (height, width) + conf_thres=0.25, # confidence threshold + iou_thres=0.45, # NMS IOU threshold + max_det=1000, # maximum detections per image + device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu + view_img=False, # show results + save_txt=False, # save results to *.txt + save_csv=False, # save results in CSV format + save_conf=False, # save confidences in --save-txt labels + save_crop=False, # save cropped prediction boxes + nosave=False, # do not save images/videos + classes=None, # filter by class: --class 0, or --class 0 2 3 + agnostic_nms=False, # class-agnostic NMS + augment=False, # augmented inference + visualize=False, # visualize features + update=False, # update all models + project=ROOT / "runs/detect", # save results to project/name + name="exp", # save results to project/name + exist_ok=False, # existing project/name ok, do not increment + line_thickness=3, # bounding box thickness (pixels) + hide_labels=False, # hide labels + hide_conf=False, # hide confidences + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + vid_stride=1, # video frame-rate stride +): + """Runs YOLOv5 detection inference on various sources like images, videos, directories, streams, etc.""" + source = str(source) + save_img = not nosave and not source.endswith(".txt") # save inference images + is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) + is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://")) + webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file) + screenshot = source.lower().startswith("screen") + if is_url and is_file: + source = check_file(source) # download + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + device = select_device(device) + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, names, pt = model.stride, model.names, model.pt + imgsz = check_img_size(imgsz, s=stride) # check image size + + # Dataloader + bs = 1 # batch_size + if webcam: + view_img = check_imshow(warn=True) + dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + bs = len(dataset) + elif screenshot: + dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) + else: + dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + vid_path, vid_writer = [None] * bs, [None] * bs + + # Run inference + model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup + seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device)) + for path, im, im0s, vid_cap, s in dataset: + with dt[0]: + im = torch.from_numpy(im).to(model.device) + im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + if len(im.shape) == 3: + im = im[None] # expand for batch dim + if model.xml and im.shape[0] > 1: + ims = torch.chunk(im, im.shape[0], 0) + + # Inference + with dt[1]: + visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False + if model.xml and im.shape[0] > 1: + pred = None + for image in ims: + if pred is None: + pred = model(image, augment=augment, visualize=visualize).unsqueeze(0) + else: + pred = torch.cat((pred, model(image, augment=augment, visualize=visualize).unsqueeze(0)), dim=0) + pred = [pred, None] + else: + pred = model(im, augment=augment, visualize=visualize) + # NMS + with dt[2]: + pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) + + # Second-stage classifier (optional) + # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) + + # Define the path for the CSV file + csv_path = save_dir / "predictions.csv" + + # Create or append to the CSV file + def write_to_csv(image_name, prediction, confidence): + """Writes prediction data for an image to a CSV file, appending if the file exists.""" + data = {"Image Name": image_name, "Prediction": prediction, "Confidence": confidence} + with open(csv_path, mode="a", newline="") as f: + writer = csv.DictWriter(f, fieldnames=data.keys()) + if not csv_path.is_file(): + writer.writeheader() + writer.writerow(data) + + # Process predictions + for i, det in enumerate(pred): # per image + seen += 1 + if webcam: # batch_size >= 1 + p, im0, frame = path[i], im0s[i].copy(), dataset.count + s += f"{i}: " + else: + p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0) + + p = Path(p) # to Path + save_path = str(save_dir / p.name) # im.jpg + txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt + s += "%gx%g " % im.shape[2:] # print string + gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh + imc = im0.copy() if save_crop else im0 # for save_crop + annotator = Annotator(im0, line_width=line_thickness, example=str(names)) + if len(det): + # Rescale boxes from img_size to im0 size + det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() + + # Print results + for c in det[:, 5].unique(): + n = (det[:, 5] == c).sum() # detections per class + s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string + + # Write results + for *xyxy, conf, cls in reversed(det): + c = int(cls) # integer class + label = names[c] if hide_conf else f"{names[c]}" + confidence = float(conf) + confidence_str = f"{confidence:.2f}" + + if save_csv: + write_to_csv(p.name, label, confidence_str) + + if save_txt: # Write to file + xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format + with open(f"{txt_path}.txt", "a") as f: + f.write(("%g " * len(line)).rstrip() % line + "\n") + + if save_img or save_crop or view_img: # Add bbox to image + c = int(cls) # integer class + label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}") + annotator.box_label(xyxy, label, color=colors(c, True)) + if save_crop: + save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True) + + # Stream results + im0 = annotator.result() + if view_img: + if platform.system() == "Linux" and p not in windows: + windows.append(p) + cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) + cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) + cv2.imshow(str(p), im0) + cv2.waitKey(1) # 1 millisecond + + # Save results (image with detections) + if save_img: + if dataset.mode == "image": + cv2.imwrite(save_path, im0) + else: # 'video' or 'stream' + if vid_path[i] != save_path: # new video + vid_path[i] = save_path + if isinstance(vid_writer[i], cv2.VideoWriter): + vid_writer[i].release() # release previous video writer + if vid_cap: # video + fps = vid_cap.get(cv2.CAP_PROP_FPS) + w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + else: # stream + fps, w, h = 30, im0.shape[1], im0.shape[0] + save_path = str(Path(save_path).with_suffix(".mp4")) # force *.mp4 suffix on results videos + vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) + vid_writer[i].write(im0) + + # Print time (inference-only) + LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") + + # Print results + t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image + LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t) + if save_txt or save_img: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else "" + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + if update: + strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) + + +def parse_opt(): + """Parses command-line arguments for YOLOv5 detection, setting inference options and model configurations.""" + parser = argparse.ArgumentParser() + parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model path or triton URL") + parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)") + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path") + parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w") + parser.add_argument("--conf-thres", type=float, default=0.25, help="confidence threshold") + parser.add_argument("--iou-thres", type=float, default=0.45, help="NMS IoU threshold") + parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--view-img", action="store_true", help="show results") + parser.add_argument("--save-txt", action="store_true", help="save results to *.txt") + parser.add_argument("--save-csv", action="store_true", help="save results in CSV format") + parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels") + parser.add_argument("--save-crop", action="store_true", help="save cropped prediction boxes") + parser.add_argument("--nosave", action="store_true", help="do not save images/videos") + parser.add_argument("--classes", nargs="+", type=int, help="filter by class: --classes 0, or --classes 0 2 3") + parser.add_argument("--agnostic-nms", action="store_true", help="class-agnostic NMS") + parser.add_argument("--augment", action="store_true", help="augmented inference") + parser.add_argument("--visualize", action="store_true", help="visualize features") + parser.add_argument("--update", action="store_true", help="update all models") + parser.add_argument("--project", default=ROOT / "runs/detect", help="save results to project/name") + parser.add_argument("--name", default="exp", help="save results to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--line-thickness", default=3, type=int, help="bounding box thickness (pixels)") + parser.add_argument("--hide-labels", default=False, action="store_true", help="hide labels") + parser.add_argument("--hide-conf", default=False, action="store_true", help="hide confidences") + parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") + parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") + parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride") + opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand + print_args(vars(opt)) + return opt + + +def main(opt): + """Executes YOLOv5 model inference with given options, checking requirements before running the model.""" + check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) + run(**vars(opt)) + + +if __name__ == "__main__": + opt = parse_opt() + main(opt) diff --git a/yolov5/export.py b/yolov5/export.py new file mode 100644 index 0000000000000000000000000000000000000000..77c972ba27f6e8fa3923381e121a8643e7b0eebe --- /dev/null +++ b/yolov5/export.py @@ -0,0 +1,947 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +""" +Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit + +Format | `export.py --include` | Model +--- | --- | --- +PyTorch | - | yolov5s.pt +TorchScript | `torchscript` | yolov5s.torchscript +ONNX | `onnx` | yolov5s.onnx +OpenVINO | `openvino` | yolov5s_openvino_model/ +TensorRT | `engine` | yolov5s.engine +CoreML | `coreml` | yolov5s.mlmodel +TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/ +TensorFlow GraphDef | `pb` | yolov5s.pb +TensorFlow Lite | `tflite` | yolov5s.tflite +TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite +TensorFlow.js | `tfjs` | yolov5s_web_model/ +PaddlePaddle | `paddle` | yolov5s_paddle_model/ + +Requirements: + $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU + $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU + +Usage: + $ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ... + +Inference: + $ python detect.py --weights yolov5s.pt # PyTorch + yolov5s.torchscript # TorchScript + yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s_openvino_model # OpenVINO + yolov5s.engine # TensorRT + yolov5s.mlmodel # CoreML (macOS-only) + yolov5s_saved_model # TensorFlow SavedModel + yolov5s.pb # TensorFlow GraphDef + yolov5s.tflite # TensorFlow Lite + yolov5s_edgetpu.tflite # TensorFlow Edge TPU + yolov5s_paddle_model # PaddlePaddle + +TensorFlow.js: + $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example + $ npm install + $ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model + $ npm start +""" + +import argparse +import contextlib +import json +import os +import platform +import re +import subprocess +import sys +import time +import warnings +from pathlib import Path + +import pandas as pd +import torch +from torch.utils.mobile_optimizer import optimize_for_mobile + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +if platform.system() != "Windows": + ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.experimental import attempt_load +from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel +from utils.dataloaders import LoadImages +from utils.general import ( + LOGGER, + Profile, + check_dataset, + check_img_size, + check_requirements, + check_version, + check_yaml, + colorstr, + file_size, + get_default_args, + print_args, + url2file, + yaml_save, +) +from utils.torch_utils import select_device, smart_inference_mode + +MACOS = platform.system() == "Darwin" # macOS environment + + +class iOSModel(torch.nn.Module): + def __init__(self, model, im): + """Initializes an iOS compatible model with normalization based on image dimensions.""" + super().__init__() + b, c, h, w = im.shape # batch, channel, height, width + self.model = model + self.nc = model.nc # number of classes + if w == h: + self.normalize = 1.0 / w + else: + self.normalize = torch.tensor([1.0 / w, 1.0 / h, 1.0 / w, 1.0 / h]) # broadcast (slower, smaller) + # np = model(im)[0].shape[1] # number of points + # self.normalize = torch.tensor([1. / w, 1. / h, 1. / w, 1. / h]).expand(np, 4) # explicit (faster, larger) + + def forward(self, x): + """Runs forward pass on the input tensor, returning class confidences and normalized coordinates.""" + xywh, conf, cls = self.model(x)[0].squeeze().split((4, 1, self.nc), 1) + return cls * conf, xywh * self.normalize # confidence (3780, 80), coordinates (3780, 4) + + +def export_formats(): + """Returns a DataFrame of supported YOLOv5 model export formats and their properties.""" + x = [ + ["PyTorch", "-", ".pt", True, True], + ["TorchScript", "torchscript", ".torchscript", True, True], + ["ONNX", "onnx", ".onnx", True, True], + ["OpenVINO", "openvino", "_openvino_model", True, False], + ["TensorRT", "engine", ".engine", False, True], + ["CoreML", "coreml", ".mlmodel", True, False], + ["TensorFlow SavedModel", "saved_model", "_saved_model", True, True], + ["TensorFlow GraphDef", "pb", ".pb", True, True], + ["TensorFlow Lite", "tflite", ".tflite", True, False], + ["TensorFlow Edge TPU", "edgetpu", "_edgetpu.tflite", False, False], + ["TensorFlow.js", "tfjs", "_web_model", False, False], + ["PaddlePaddle", "paddle", "_paddle_model", True, True], + ] + return pd.DataFrame(x, columns=["Format", "Argument", "Suffix", "CPU", "GPU"]) + + +def try_export(inner_func): + """Decorator @try_export for YOLOv5 model export functions that logs success/failure, time taken, and file size.""" + inner_args = get_default_args(inner_func) + + def outer_func(*args, **kwargs): + """Logs success/failure and execution details of model export functions wrapped with @try_export decorator.""" + prefix = inner_args["prefix"] + try: + with Profile() as dt: + f, model = inner_func(*args, **kwargs) + LOGGER.info(f"{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)") + return f, model + except Exception as e: + LOGGER.info(f"{prefix} export failure ❌ {dt.t:.1f}s: {e}") + return None, None + + return outer_func + + +@try_export +def export_torchscript(model, im, file, optimize, prefix=colorstr("TorchScript:")): + """Exports YOLOv5 model to TorchScript format, optionally optimized for mobile, with image shape and stride + metadata. + """ + LOGGER.info(f"\n{prefix} starting export with torch {torch.__version__}...") + f = file.with_suffix(".torchscript") + + ts = torch.jit.trace(model, im, strict=False) + d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names} + extra_files = {"config.txt": json.dumps(d)} # torch._C.ExtraFilesMap() + if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html + optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files) + else: + ts.save(str(f), _extra_files=extra_files) + return f, None + + +@try_export +def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr("ONNX:")): + """Exports a YOLOv5 model to ONNX format with dynamic axes and optional simplification.""" + check_requirements("onnx>=1.12.0") + import onnx + + LOGGER.info(f"\n{prefix} starting export with onnx {onnx.__version__}...") + f = str(file.with_suffix(".onnx")) + + output_names = ["output0", "output1"] if isinstance(model, SegmentationModel) else ["output0"] + if dynamic: + dynamic = {"images": {0: "batch", 2: "height", 3: "width"}} # shape(1,3,640,640) + if isinstance(model, SegmentationModel): + dynamic["output0"] = {0: "batch", 1: "anchors"} # shape(1,25200,85) + dynamic["output1"] = {0: "batch", 2: "mask_height", 3: "mask_width"} # shape(1,32,160,160) + elif isinstance(model, DetectionModel): + dynamic["output0"] = {0: "batch", 1: "anchors"} # shape(1,25200,85) + + torch.onnx.export( + model.cpu() if dynamic else model, # --dynamic only compatible with cpu + im.cpu() if dynamic else im, + f, + verbose=False, + opset_version=opset, + do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False + input_names=["images"], + output_names=output_names, + dynamic_axes=dynamic or None, + ) + + # Checks + model_onnx = onnx.load(f) # load onnx model + onnx.checker.check_model(model_onnx) # check onnx model + + # Metadata + d = {"stride": int(max(model.stride)), "names": model.names} + for k, v in d.items(): + meta = model_onnx.metadata_props.add() + meta.key, meta.value = k, str(v) + onnx.save(model_onnx, f) + + # Simplify + if simplify: + try: + cuda = torch.cuda.is_available() + check_requirements(("onnxruntime-gpu" if cuda else "onnxruntime", "onnx-simplifier>=0.4.1")) + import onnxsim + + LOGGER.info(f"{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...") + model_onnx, check = onnxsim.simplify(model_onnx) + assert check, "assert check failed" + onnx.save(model_onnx, f) + except Exception as e: + LOGGER.info(f"{prefix} simplifier failure: {e}") + return f, model_onnx + + +@try_export +def export_openvino(file, metadata, half, int8, data, prefix=colorstr("OpenVINO:")): + """Exports a YOLOv5 model to OpenVINO format with optional FP16 and INT8 quantization; see https://pypi.org/project/openvino-dev/.""" + check_requirements("openvino-dev>=2023.0") # requires openvino-dev: https://pypi.org/project/openvino-dev/ + import openvino.runtime as ov # noqa + from openvino.tools import mo # noqa + + LOGGER.info(f"\n{prefix} starting export with openvino {ov.__version__}...") + f = str(file).replace(file.suffix, f"_{'int8_' if int8 else ''}openvino_model{os.sep}") + f_onnx = file.with_suffix(".onnx") + f_ov = str(Path(f) / file.with_suffix(".xml").name) + + ov_model = mo.convert_model(f_onnx, model_name=file.stem, framework="onnx", compress_to_fp16=half) # export + + if int8: + check_requirements("nncf>=2.5.0") # requires at least version 2.5.0 to use the post-training quantization + import nncf + import numpy as np + + from utils.dataloaders import create_dataloader + + def gen_dataloader(yaml_path, task="train", imgsz=640, workers=4): + """Generates a DataLoader for model training or validation based on the given YAML dataset configuration.""" + data_yaml = check_yaml(yaml_path) + data = check_dataset(data_yaml) + dataloader = create_dataloader( + data[task], imgsz=imgsz, batch_size=1, stride=32, pad=0.5, single_cls=False, rect=False, workers=workers + )[0] + return dataloader + + # noqa: F811 + + def transform_fn(data_item): + """ + Quantization transform function. + + Extracts and preprocess input data from dataloader item for quantization. + Parameters: + data_item: Tuple with data item produced by DataLoader during iteration + Returns: + input_tensor: Input data for quantization + """ + assert data_item[0].dtype == torch.uint8, "input image must be uint8 for the quantization preprocessing" + + img = data_item[0].numpy().astype(np.float32) # uint8 to fp16/32 + img /= 255.0 # 0 - 255 to 0.0 - 1.0 + return np.expand_dims(img, 0) if img.ndim == 3 else img + + ds = gen_dataloader(data) + quantization_dataset = nncf.Dataset(ds, transform_fn) + ov_model = nncf.quantize(ov_model, quantization_dataset, preset=nncf.QuantizationPreset.MIXED) + + ov.serialize(ov_model, f_ov) # save + yaml_save(Path(f) / file.with_suffix(".yaml").name, metadata) # add metadata.yaml + return f, None + + +@try_export +def export_paddle(model, im, file, metadata, prefix=colorstr("PaddlePaddle:")): + """Exports a YOLOv5 model to PaddlePaddle format using X2Paddle, saving to `save_dir` and adding a metadata.yaml + file. + """ + check_requirements(("paddlepaddle", "x2paddle")) + import x2paddle + from x2paddle.convert import pytorch2paddle + + LOGGER.info(f"\n{prefix} starting export with X2Paddle {x2paddle.__version__}...") + f = str(file).replace(".pt", f"_paddle_model{os.sep}") + + pytorch2paddle(module=model, save_dir=f, jit_type="trace", input_examples=[im]) # export + yaml_save(Path(f) / file.with_suffix(".yaml").name, metadata) # add metadata.yaml + return f, None + + +@try_export +def export_coreml(model, im, file, int8, half, nms, prefix=colorstr("CoreML:")): + """Exports YOLOv5 model to CoreML format with optional NMS, INT8, and FP16 support; requires coremltools.""" + check_requirements("coremltools") + import coremltools as ct + + LOGGER.info(f"\n{prefix} starting export with coremltools {ct.__version__}...") + f = file.with_suffix(".mlmodel") + + if nms: + model = iOSModel(model, im) + ts = torch.jit.trace(model, im, strict=False) # TorchScript model + ct_model = ct.convert(ts, inputs=[ct.ImageType("image", shape=im.shape, scale=1 / 255, bias=[0, 0, 0])]) + bits, mode = (8, "kmeans_lut") if int8 else (16, "linear") if half else (32, None) + if bits < 32: + if MACOS: # quantization only supported on macOS + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=DeprecationWarning) # suppress numpy==1.20 float warning + ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode) + else: + print(f"{prefix} quantization only supported on macOS, skipping...") + ct_model.save(f) + return f, ct_model + + +@try_export +def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr("TensorRT:")): + """ + Exports a YOLOv5 model to TensorRT engine format, requiring GPU and TensorRT>=7.0.0. + + https://developer.nvidia.com/tensorrt + """ + assert im.device.type != "cpu", "export running on CPU but must be on GPU, i.e. `python export.py --device 0`" + try: + import tensorrt as trt + except Exception: + if platform.system() == "Linux": + check_requirements("nvidia-tensorrt", cmds="-U --index-url https://pypi.ngc.nvidia.com") + import tensorrt as trt + + if trt.__version__[0] == "7": # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012 + grid = model.model[-1].anchor_grid + model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid] + export_onnx(model, im, file, 12, dynamic, simplify) # opset 12 + model.model[-1].anchor_grid = grid + else: # TensorRT >= 8 + check_version(trt.__version__, "8.0.0", hard=True) # require tensorrt>=8.0.0 + export_onnx(model, im, file, 12, dynamic, simplify) # opset 12 + onnx = file.with_suffix(".onnx") + + LOGGER.info(f"\n{prefix} starting export with TensorRT {trt.__version__}...") + is_trt10 = int(trt.__version__.split(".")[0]) >= 10 # is TensorRT >= 10 + assert onnx.exists(), f"failed to export ONNX file: {onnx}" + f = file.with_suffix(".engine") # TensorRT engine file + logger = trt.Logger(trt.Logger.INFO) + if verbose: + logger.min_severity = trt.Logger.Severity.VERBOSE + + builder = trt.Builder(logger) + config = builder.create_builder_config() + if is_trt10: + config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) + else: # TensorRT versions 7, 8 + config.max_workspace_size = workspace * 1 << 30 + flag = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) + network = builder.create_network(flag) + parser = trt.OnnxParser(network, logger) + if not parser.parse_from_file(str(onnx)): + raise RuntimeError(f"failed to load ONNX file: {onnx}") + + inputs = [network.get_input(i) for i in range(network.num_inputs)] + outputs = [network.get_output(i) for i in range(network.num_outputs)] + for inp in inputs: + LOGGER.info(f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}') + for out in outputs: + LOGGER.info(f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}') + + if dynamic: + if im.shape[0] <= 1: + LOGGER.warning(f"{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument") + profile = builder.create_optimization_profile() + for inp in inputs: + profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape) + config.add_optimization_profile(profile) + + LOGGER.info(f"{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}") + if builder.platform_has_fast_fp16 and half: + config.set_flag(trt.BuilderFlag.FP16) + + build = builder.build_serialized_network if is_trt10 else builder.build_engine + with build(network, config) as engine, open(f, "wb") as t: + t.write(engine if is_trt10 else engine.serialize()) + return f, None + + +@try_export +def export_saved_model( + model, + im, + file, + dynamic, + tf_nms=False, + agnostic_nms=False, + topk_per_class=100, + topk_all=100, + iou_thres=0.45, + conf_thres=0.25, + keras=False, + prefix=colorstr("TensorFlow SavedModel:"), +): + """Exports a YOLOv5 model to TensorFlow SavedModel format, supporting dynamic axes and non-maximum suppression + (NMS). + """ + # YOLOv5 TensorFlow SavedModel export + try: + import tensorflow as tf + except Exception: + check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}<=2.15.1") + + import tensorflow as tf + from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 + + from models.tf import TFModel + + LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") + if tf.__version__ > "2.13.1": + helper_url = "https://github.com/ultralytics/yolov5/issues/12489" + LOGGER.info( + f"WARNING ⚠️ using Tensorflow {tf.__version__} > 2.13.1 might cause issue when exporting the model to tflite {helper_url}" + ) # handling issue https://github.com/ultralytics/yolov5/issues/12489 + f = str(file).replace(".pt", "_saved_model") + batch_size, ch, *imgsz = list(im.shape) # BCHW + + tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz) + im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow + _ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres) + inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size) + outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres) + keras_model = tf.keras.Model(inputs=inputs, outputs=outputs) + keras_model.trainable = False + keras_model.summary() + if keras: + keras_model.save(f, save_format="tf") + else: + spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype) + m = tf.function(lambda x: keras_model(x)) # full model + m = m.get_concrete_function(spec) + frozen_func = convert_variables_to_constants_v2(m) + tfm = tf.Module() + tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x), [spec]) + tfm.__call__(im) + tf.saved_model.save( + tfm, + f, + options=tf.saved_model.SaveOptions(experimental_custom_gradients=False) + if check_version(tf.__version__, "2.6") + else tf.saved_model.SaveOptions(), + ) + return f, keras_model + + +@try_export +def export_pb(keras_model, file, prefix=colorstr("TensorFlow GraphDef:")): + """Exports YOLOv5 model to TensorFlow GraphDef *.pb format; see https://github.com/leimao/Frozen_Graph_TensorFlow for details.""" + import tensorflow as tf + from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 + + LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") + f = file.with_suffix(".pb") + + m = tf.function(lambda x: keras_model(x)) # full model + m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)) + frozen_func = convert_variables_to_constants_v2(m) + frozen_func.graph.as_graph_def() + tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False) + return f, None + + +@try_export +def export_tflite( + keras_model, im, file, int8, per_tensor, data, nms, agnostic_nms, prefix=colorstr("TensorFlow Lite:") +): + # YOLOv5 TensorFlow Lite export + """Exports YOLOv5 model to TensorFlow Lite format with optional FP16, INT8, and NMS support.""" + import tensorflow as tf + + LOGGER.info(f"\n{prefix} starting export with tensorflow {tf.__version__}...") + batch_size, ch, *imgsz = list(im.shape) # BCHW + f = str(file).replace(".pt", "-fp16.tflite") + + converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) + converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] + converter.target_spec.supported_types = [tf.float16] + converter.optimizations = [tf.lite.Optimize.DEFAULT] + if int8: + from models.tf import representative_dataset_gen + + dataset = LoadImages(check_dataset(check_yaml(data))["train"], img_size=imgsz, auto=False) + converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100) + converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] + converter.target_spec.supported_types = [] + converter.inference_input_type = tf.uint8 # or tf.int8 + converter.inference_output_type = tf.uint8 # or tf.int8 + converter.experimental_new_quantizer = True + if per_tensor: + converter._experimental_disable_per_channel = True + f = str(file).replace(".pt", "-int8.tflite") + if nms or agnostic_nms: + converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS) + + tflite_model = converter.convert() + open(f, "wb").write(tflite_model) + return f, None + + +@try_export +def export_edgetpu(file, prefix=colorstr("Edge TPU:")): + """ + Exports a YOLOv5 model to Edge TPU compatible TFLite format; requires Linux and Edge TPU compiler. + + https://coral.ai/docs/edgetpu/models-intro/ + """ + cmd = "edgetpu_compiler --version" + help_url = "https://coral.ai/docs/edgetpu/compiler/" + assert platform.system() == "Linux", f"export only supported on Linux. See {help_url}" + if subprocess.run(f"{cmd} > /dev/null 2>&1", shell=True).returncode != 0: + LOGGER.info(f"\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}") + sudo = subprocess.run("sudo --version >/dev/null", shell=True).returncode == 0 # sudo installed on system + for c in ( + "curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -", + 'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list', + "sudo apt-get update", + "sudo apt-get install edgetpu-compiler", + ): + subprocess.run(c if sudo else c.replace("sudo ", ""), shell=True, check=True) + ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1] + + LOGGER.info(f"\n{prefix} starting export with Edge TPU compiler {ver}...") + f = str(file).replace(".pt", "-int8_edgetpu.tflite") # Edge TPU model + f_tfl = str(file).replace(".pt", "-int8.tflite") # TFLite model + + subprocess.run( + [ + "edgetpu_compiler", + "-s", + "-d", + "-k", + "10", + "--out_dir", + str(file.parent), + f_tfl, + ], + check=True, + ) + return f, None + + +@try_export +def export_tfjs(file, int8, prefix=colorstr("TensorFlow.js:")): + """Exports a YOLOv5 model to TensorFlow.js format, optionally with uint8 quantization.""" + check_requirements("tensorflowjs") + import tensorflowjs as tfjs + + LOGGER.info(f"\n{prefix} starting export with tensorflowjs {tfjs.__version__}...") + f = str(file).replace(".pt", "_web_model") # js dir + f_pb = file.with_suffix(".pb") # *.pb path + f_json = f"{f}/model.json" # *.json path + + args = [ + "tensorflowjs_converter", + "--input_format=tf_frozen_model", + "--quantize_uint8" if int8 else "", + "--output_node_names=Identity,Identity_1,Identity_2,Identity_3", + str(f_pb), + f, + ] + subprocess.run([arg for arg in args if arg], check=True) + + json = Path(f_json).read_text() + with open(f_json, "w") as j: # sort JSON Identity_* in ascending order + subst = re.sub( + r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, ' + r'"Identity.?.?": {"name": "Identity.?.?"}, ' + r'"Identity.?.?": {"name": "Identity.?.?"}, ' + r'"Identity.?.?": {"name": "Identity.?.?"}}}', + r'{"outputs": {"Identity": {"name": "Identity"}, ' + r'"Identity_1": {"name": "Identity_1"}, ' + r'"Identity_2": {"name": "Identity_2"}, ' + r'"Identity_3": {"name": "Identity_3"}}}', + json, + ) + j.write(subst) + return f, None + + +def add_tflite_metadata(file, metadata, num_outputs): + """ + Adds TFLite metadata to a model file, supporting multiple outputs, as specified by TensorFlow guidelines. + + https://www.tensorflow.org/lite/models/convert/metadata + """ + with contextlib.suppress(ImportError): + # check_requirements('tflite_support') + from tflite_support import flatbuffers + from tflite_support import metadata as _metadata + from tflite_support import metadata_schema_py_generated as _metadata_fb + + tmp_file = Path("/tmp/meta.txt") + with open(tmp_file, "w") as meta_f: + meta_f.write(str(metadata)) + + model_meta = _metadata_fb.ModelMetadataT() + label_file = _metadata_fb.AssociatedFileT() + label_file.name = tmp_file.name + model_meta.associatedFiles = [label_file] + + subgraph = _metadata_fb.SubGraphMetadataT() + subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()] + subgraph.outputTensorMetadata = [_metadata_fb.TensorMetadataT()] * num_outputs + model_meta.subgraphMetadata = [subgraph] + + b = flatbuffers.Builder(0) + b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER) + metadata_buf = b.Output() + + populator = _metadata.MetadataPopulator.with_model_file(file) + populator.load_metadata_buffer(metadata_buf) + populator.load_associated_files([str(tmp_file)]) + populator.populate() + tmp_file.unlink() + + +def pipeline_coreml(model, im, file, names, y, prefix=colorstr("CoreML Pipeline:")): + """Converts a PyTorch YOLOv5 model to CoreML format with NMS, handling different input/output shapes and saving the + model. + """ + import coremltools as ct + from PIL import Image + + print(f"{prefix} starting pipeline with coremltools {ct.__version__}...") + batch_size, ch, h, w = list(im.shape) # BCHW + t = time.time() + + # YOLOv5 Output shapes + spec = model.get_spec() + out0, out1 = iter(spec.description.output) + if platform.system() == "Darwin": + img = Image.new("RGB", (w, h)) # img(192 width, 320 height) + # img = torch.zeros((*opt.img_size, 3)).numpy() # img size(320,192,3) iDetection + out = model.predict({"image": img}) + out0_shape, out1_shape = out[out0.name].shape, out[out1.name].shape + else: # linux and windows can not run model.predict(), get sizes from pytorch output y + s = tuple(y[0].shape) + out0_shape, out1_shape = (s[1], s[2] - 5), (s[1], 4) # (3780, 80), (3780, 4) + + # Checks + nx, ny = spec.description.input[0].type.imageType.width, spec.description.input[0].type.imageType.height + na, nc = out0_shape + # na, nc = out0.type.multiArrayType.shape # number anchors, classes + assert len(names) == nc, f"{len(names)} names found for nc={nc}" # check + + # Define output shapes (missing) + out0.type.multiArrayType.shape[:] = out0_shape # (3780, 80) + out1.type.multiArrayType.shape[:] = out1_shape # (3780, 4) + # spec.neuralNetwork.preprocessing[0].featureName = '0' + + # Flexible input shapes + # from coremltools.models.neural_network import flexible_shape_utils + # s = [] # shapes + # s.append(flexible_shape_utils.NeuralNetworkImageSize(320, 192)) + # s.append(flexible_shape_utils.NeuralNetworkImageSize(640, 384)) # (height, width) + # flexible_shape_utils.add_enumerated_image_sizes(spec, feature_name='image', sizes=s) + # r = flexible_shape_utils.NeuralNetworkImageSizeRange() # shape ranges + # r.add_height_range((192, 640)) + # r.add_width_range((192, 640)) + # flexible_shape_utils.update_image_size_range(spec, feature_name='image', size_range=r) + + # Print + print(spec.description) + + # Model from spec + model = ct.models.MLModel(spec) + + # 3. Create NMS protobuf + nms_spec = ct.proto.Model_pb2.Model() + nms_spec.specificationVersion = 5 + for i in range(2): + decoder_output = model._spec.description.output[i].SerializeToString() + nms_spec.description.input.add() + nms_spec.description.input[i].ParseFromString(decoder_output) + nms_spec.description.output.add() + nms_spec.description.output[i].ParseFromString(decoder_output) + + nms_spec.description.output[0].name = "confidence" + nms_spec.description.output[1].name = "coordinates" + + output_sizes = [nc, 4] + for i in range(2): + ma_type = nms_spec.description.output[i].type.multiArrayType + ma_type.shapeRange.sizeRanges.add() + ma_type.shapeRange.sizeRanges[0].lowerBound = 0 + ma_type.shapeRange.sizeRanges[0].upperBound = -1 + ma_type.shapeRange.sizeRanges.add() + ma_type.shapeRange.sizeRanges[1].lowerBound = output_sizes[i] + ma_type.shapeRange.sizeRanges[1].upperBound = output_sizes[i] + del ma_type.shape[:] + + nms = nms_spec.nonMaximumSuppression + nms.confidenceInputFeatureName = out0.name # 1x507x80 + nms.coordinatesInputFeatureName = out1.name # 1x507x4 + nms.confidenceOutputFeatureName = "confidence" + nms.coordinatesOutputFeatureName = "coordinates" + nms.iouThresholdInputFeatureName = "iouThreshold" + nms.confidenceThresholdInputFeatureName = "confidenceThreshold" + nms.iouThreshold = 0.45 + nms.confidenceThreshold = 0.25 + nms.pickTop.perClass = True + nms.stringClassLabels.vector.extend(names.values()) + nms_model = ct.models.MLModel(nms_spec) + + # 4. Pipeline models together + pipeline = ct.models.pipeline.Pipeline( + input_features=[ + ("image", ct.models.datatypes.Array(3, ny, nx)), + ("iouThreshold", ct.models.datatypes.Double()), + ("confidenceThreshold", ct.models.datatypes.Double()), + ], + output_features=["confidence", "coordinates"], + ) + pipeline.add_model(model) + pipeline.add_model(nms_model) + + # Correct datatypes + pipeline.spec.description.input[0].ParseFromString(model._spec.description.input[0].SerializeToString()) + pipeline.spec.description.output[0].ParseFromString(nms_model._spec.description.output[0].SerializeToString()) + pipeline.spec.description.output[1].ParseFromString(nms_model._spec.description.output[1].SerializeToString()) + + # Update metadata + pipeline.spec.specificationVersion = 5 + pipeline.spec.description.metadata.versionString = "https://github.com/ultralytics/yolov5" + pipeline.spec.description.metadata.shortDescription = "https://github.com/ultralytics/yolov5" + pipeline.spec.description.metadata.author = "glenn.jocher@ultralytics.com" + pipeline.spec.description.metadata.license = "https://github.com/ultralytics/yolov5/blob/master/LICENSE" + pipeline.spec.description.metadata.userDefined.update( + { + "classes": ",".join(names.values()), + "iou_threshold": str(nms.iouThreshold), + "confidence_threshold": str(nms.confidenceThreshold), + } + ) + + # Save the model + f = file.with_suffix(".mlmodel") # filename + model = ct.models.MLModel(pipeline.spec) + model.input_description["image"] = "Input image" + model.input_description["iouThreshold"] = f"(optional) IOU Threshold override (default: {nms.iouThreshold})" + model.input_description["confidenceThreshold"] = ( + f"(optional) Confidence Threshold override (default: {nms.confidenceThreshold})" + ) + model.output_description["confidence"] = 'Boxes × Class confidence (see user-defined metadata "classes")' + model.output_description["coordinates"] = "Boxes × [x, y, width, height] (relative to image size)" + model.save(f) # pipelined + print(f"{prefix} pipeline success ({time.time() - t:.2f}s), saved as {f} ({file_size(f):.1f} MB)") + + +@smart_inference_mode() +def run( + data=ROOT / "data/coco128.yaml", # 'dataset.yaml path' + weights=ROOT / "yolov5s.pt", # weights path + imgsz=(640, 640), # image (height, width) + batch_size=1, # batch size + device="cpu", # cuda device, i.e. 0 or 0,1,2,3 or cpu + include=("torchscript", "onnx"), # include formats + half=False, # FP16 half-precision export + inplace=False, # set YOLOv5 Detect() inplace=True + keras=False, # use Keras + optimize=False, # TorchScript: optimize for mobile + int8=False, # CoreML/TF INT8 quantization + per_tensor=False, # TF per tensor quantization + dynamic=False, # ONNX/TF/TensorRT: dynamic axes + simplify=False, # ONNX: simplify model + opset=12, # ONNX: opset version + verbose=False, # TensorRT: verbose log + workspace=4, # TensorRT: workspace size (GB) + nms=False, # TF: add NMS to model + agnostic_nms=False, # TF: add agnostic NMS to model + topk_per_class=100, # TF.js NMS: topk per class to keep + topk_all=100, # TF.js NMS: topk for all classes to keep + iou_thres=0.45, # TF.js NMS: IoU threshold + conf_thres=0.25, # TF.js NMS: confidence threshold +): + """Exports YOLOv5 model to specified formats including ONNX, TensorRT, CoreML, and TensorFlow; see https://github.com/ultralytics/yolov5.""" + t = time.time() + include = [x.lower() for x in include] # to lowercase + fmts = tuple(export_formats()["Argument"][1:]) # --include arguments + flags = [x in include for x in fmts] + assert sum(flags) == len(include), f"ERROR: Invalid --include {include}, valid --include arguments are {fmts}" + jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags # export booleans + file = Path(url2file(weights) if str(weights).startswith(("http:/", "https:/")) else weights) # PyTorch weights + + # Load PyTorch model + device = select_device(device) + if half: + assert device.type != "cpu" or coreml, "--half only compatible with GPU export, i.e. use --device 0" + assert not dynamic, "--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both" + model = attempt_load(weights, device=device, inplace=True, fuse=True) # load FP32 model + + # Checks + imgsz *= 2 if len(imgsz) == 1 else 1 # expand + if optimize: + assert device.type == "cpu", "--optimize not compatible with cuda devices, i.e. use --device cpu" + + # Input + gs = int(max(model.stride)) # grid size (max stride) + imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples + im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection + + # Update model + model.eval() + for k, m in model.named_modules(): + if isinstance(m, Detect): + m.inplace = inplace + m.dynamic = dynamic + m.export = True + + for _ in range(2): + y = model(im) # dry runs + if half and not coreml: + im, model = im.half(), model.half() # to FP16 + shape = tuple((y[0] if isinstance(y, tuple) else y).shape) # model output shape + metadata = {"stride": int(max(model.stride)), "names": model.names} # model metadata + LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)") + + # Exports + f = [""] * len(fmts) # exported filenames + warnings.filterwarnings(action="ignore", category=torch.jit.TracerWarning) # suppress TracerWarning + if jit: # TorchScript + f[0], _ = export_torchscript(model, im, file, optimize) + if engine: # TensorRT required before ONNX + f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose) + if onnx or xml: # OpenVINO requires ONNX + f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify) + if xml: # OpenVINO + f[3], _ = export_openvino(file, metadata, half, int8, data) + if coreml: # CoreML + f[4], ct_model = export_coreml(model, im, file, int8, half, nms) + if nms: + pipeline_coreml(ct_model, im, file, model.names, y) + if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats + assert not tflite or not tfjs, "TFLite and TF.js models must be exported separately, please pass only one type." + assert not isinstance(model, ClassificationModel), "ClassificationModel export to TF formats not yet supported." + f[5], s_model = export_saved_model( + model.cpu(), + im, + file, + dynamic, + tf_nms=nms or agnostic_nms or tfjs, + agnostic_nms=agnostic_nms or tfjs, + topk_per_class=topk_per_class, + topk_all=topk_all, + iou_thres=iou_thres, + conf_thres=conf_thres, + keras=keras, + ) + if pb or tfjs: # pb prerequisite to tfjs + f[6], _ = export_pb(s_model, file) + if tflite or edgetpu: + f[7], _ = export_tflite( + s_model, im, file, int8 or edgetpu, per_tensor, data=data, nms=nms, agnostic_nms=agnostic_nms + ) + if edgetpu: + f[8], _ = export_edgetpu(file) + add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs)) + if tfjs: + f[9], _ = export_tfjs(file, int8) + if paddle: # PaddlePaddle + f[10], _ = export_paddle(model, im, file, metadata) + + # Finish + f = [str(x) for x in f if x] # filter out '' and None + if any(f): + cls, det, seg = (isinstance(model, x) for x in (ClassificationModel, DetectionModel, SegmentationModel)) # type + det &= not seg # segmentation models inherit from SegmentationModel(DetectionModel) + dir = Path("segment" if seg else "classify" if cls else "") + h = "--half" if half else "" # --half FP16 inference arg + s = ( + "# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference" + if cls + else "# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference" + if seg + else "" + ) + LOGGER.info( + f'\nExport complete ({time.time() - t:.1f}s)' + f"\nResults saved to {colorstr('bold', file.parent.resolve())}" + f"\nDetect: python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}" + f"\nValidate: python {dir / 'val.py'} --weights {f[-1]} {h}" + f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}') {s}" + f'\nVisualize: https://netron.app' + ) + return f # return list of exported files/dirs + + +def parse_opt(known=False): + """Parses command-line arguments for YOLOv5 model export configurations, returning the parsed options.""" + parser = argparse.ArgumentParser() + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") + parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model.pt path(s)") + parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640, 640], help="image (h, w)") + parser.add_argument("--batch-size", type=int, default=1, help="batch size") + parser.add_argument("--device", default="cpu", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--half", action="store_true", help="FP16 half-precision export") + parser.add_argument("--inplace", action="store_true", help="set YOLOv5 Detect() inplace=True") + parser.add_argument("--keras", action="store_true", help="TF: use Keras") + parser.add_argument("--optimize", action="store_true", help="TorchScript: optimize for mobile") + parser.add_argument("--int8", action="store_true", help="CoreML/TF/OpenVINO INT8 quantization") + parser.add_argument("--per-tensor", action="store_true", help="TF per-tensor quantization") + parser.add_argument("--dynamic", action="store_true", help="ONNX/TF/TensorRT: dynamic axes") + parser.add_argument("--simplify", action="store_true", help="ONNX: simplify model") + parser.add_argument("--opset", type=int, default=17, help="ONNX: opset version") + parser.add_argument("--verbose", action="store_true", help="TensorRT: verbose log") + parser.add_argument("--workspace", type=int, default=4, help="TensorRT: workspace size (GB)") + parser.add_argument("--nms", action="store_true", help="TF: add NMS to model") + parser.add_argument("--agnostic-nms", action="store_true", help="TF: add agnostic NMS to model") + parser.add_argument("--topk-per-class", type=int, default=100, help="TF.js NMS: topk per class to keep") + parser.add_argument("--topk-all", type=int, default=100, help="TF.js NMS: topk for all classes to keep") + parser.add_argument("--iou-thres", type=float, default=0.45, help="TF.js NMS: IoU threshold") + parser.add_argument("--conf-thres", type=float, default=0.25, help="TF.js NMS: confidence threshold") + parser.add_argument( + "--include", + nargs="+", + default=["torchscript"], + help="torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle", + ) + opt = parser.parse_known_args()[0] if known else parser.parse_args() + print_args(vars(opt)) + return opt + + +def main(opt): + """Executes the YOLOv5 model inference or export with specified weights and options.""" + for opt.weights in opt.weights if isinstance(opt.weights, list) else [opt.weights]: + run(**vars(opt)) + + +if __name__ == "__main__": + opt = parse_opt() + main(opt) diff --git a/yolov5/hubconf.py b/yolov5/hubconf.py new file mode 100644 index 0000000000000000000000000000000000000000..4b0c36b8daed41ee4fa4b4833cddec9f1c53a81d --- /dev/null +++ b/yolov5/hubconf.py @@ -0,0 +1,195 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +""" +PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5 + +Usage: + import torch + model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # official model + model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s') # from branch + model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt') # custom/local model + model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local') # local repo +""" + +import torch + + +def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): + """ + Creates or loads a YOLOv5 model. + + Arguments: + name (str): model name 'yolov5s' or path 'path/to/best.pt' + pretrained (bool): load pretrained weights into the model + channels (int): number of input channels + classes (int): number of model classes + autoshape (bool): apply YOLOv5 .autoshape() wrapper to model + verbose (bool): print all information to screen + device (str, torch.device, None): device to use for model parameters + + Returns: + YOLOv5 model + """ + from pathlib import Path + + from models.common import AutoShape, DetectMultiBackend + from models.experimental import attempt_load + from models.yolo import ClassificationModel, DetectionModel, SegmentationModel + from utils.downloads import attempt_download + from utils.general import LOGGER, ROOT, check_requirements, intersect_dicts, logging + from utils.torch_utils import select_device + + if not verbose: + LOGGER.setLevel(logging.WARNING) + check_requirements(ROOT / "requirements.txt", exclude=("opencv-python", "tensorboard", "thop")) + name = Path(name) + path = name.with_suffix(".pt") if name.suffix == "" and not name.is_dir() else name # checkpoint path + try: + device = select_device(device) + if pretrained and channels == 3 and classes == 80: + try: + model = DetectMultiBackend(path, device=device, fuse=autoshape) # detection model + if autoshape: + if model.pt and isinstance(model.model, ClassificationModel): + LOGGER.warning( + "WARNING ⚠️ YOLOv5 ClassificationModel is not yet AutoShape compatible. " + "You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224)." + ) + elif model.pt and isinstance(model.model, SegmentationModel): + LOGGER.warning( + "WARNING ⚠️ YOLOv5 SegmentationModel is not yet AutoShape compatible. " + "You will not be able to run inference with this model." + ) + else: + model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS + except Exception: + model = attempt_load(path, device=device, fuse=False) # arbitrary model + else: + cfg = list((Path(__file__).parent / "models").rglob(f"{path.stem}.yaml"))[0] # model.yaml path + model = DetectionModel(cfg, channels, classes) # create model + if pretrained: + ckpt = torch.load(attempt_download(path), map_location=device) # load + csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32 + csd = intersect_dicts(csd, model.state_dict(), exclude=["anchors"]) # intersect + model.load_state_dict(csd, strict=False) # load + if len(ckpt["model"].names) == classes: + model.names = ckpt["model"].names # set class names attribute + if not verbose: + LOGGER.setLevel(logging.INFO) # reset to default + return model.to(device) + + except Exception as e: + help_url = "https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading" + s = f"{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help." + raise Exception(s) from e + + +def custom(path="path/to/model.pt", autoshape=True, _verbose=True, device=None): + """Loads a custom or local YOLOv5 model from a given path with optional autoshaping and device specification.""" + return _create(path, autoshape=autoshape, verbose=_verbose, device=device) + + +def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + """Instantiates the YOLOv5-nano model with options for pretraining, input channels, class count, autoshaping, + verbosity, and device. + """ + return _create("yolov5n", pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + """Creates YOLOv5-small model with options for pretraining, input channels, class count, autoshaping, verbosity, and + device. + """ + return _create("yolov5s", pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + """Instantiates the YOLOv5-medium model with customizable pretraining, channel count, class count, autoshaping, + verbosity, and device. + """ + return _create("yolov5m", pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + """Creates YOLOv5-large model with options for pretraining, channels, classes, autoshaping, verbosity, and device + selection. + """ + return _create("yolov5l", pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + """Instantiates the YOLOv5-xlarge model with customizable pretraining, channel count, class count, autoshaping, + verbosity, and device. + """ + return _create("yolov5x", pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + """Creates YOLOv5-nano-P6 model with options for pretraining, channels, classes, autoshaping, verbosity, and + device. + """ + return _create("yolov5n6", pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + """Instantiate YOLOv5-small-P6 model with options for pretraining, input channels, number of classes, autoshaping, + verbosity, and device selection. + """ + return _create("yolov5s6", pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + """Creates YOLOv5-medium-P6 model with options for pretraining, channel count, class count, autoshaping, verbosity, + and device. + """ + return _create("yolov5m6", pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + """Instantiates the YOLOv5-large-P6 model with customizable pretraining, channel and class counts, autoshaping, + verbosity, and device selection. + """ + return _create("yolov5l6", pretrained, channels, classes, autoshape, _verbose, device) + + +def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None): + """Creates YOLOv5-xlarge-P6 model with options for pretraining, channels, classes, autoshaping, verbosity, and + device. + """ + return _create("yolov5x6", pretrained, channels, classes, autoshape, _verbose, device) + + +if __name__ == "__main__": + import argparse + from pathlib import Path + + import numpy as np + from PIL import Image + + from utils.general import cv2, print_args + + # Argparser + parser = argparse.ArgumentParser() + parser.add_argument("--model", type=str, default="yolov5s", help="model name") + opt = parser.parse_args() + print_args(vars(opt)) + + # Model + model = _create(name=opt.model, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) + # model = custom(path='path/to/model.pt') # custom + + # Images + imgs = [ + "data/images/zidane.jpg", # filename + Path("data/images/zidane.jpg"), # Path + "https://ultralytics.com/images/zidane.jpg", # URI + cv2.imread("data/images/bus.jpg")[:, :, ::-1], # OpenCV + Image.open("data/images/bus.jpg"), # PIL + np.zeros((320, 640, 3)), + ] # numpy + + # Inference + results = model(imgs, size=320) # batched inference + + # Results + results.print() + results.save() diff --git a/yolov5/models/__init__.py b/yolov5/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/yolov5/models/__pycache__/__init__.cpython-311.pyc b/yolov5/models/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c0c55c58c02ccc836d21bd7119b8d1eac32bdef2 Binary files /dev/null and b/yolov5/models/__pycache__/__init__.cpython-311.pyc differ diff --git a/yolov5/models/__pycache__/experimental.cpython-311.pyc b/yolov5/models/__pycache__/experimental.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ae990022963aa3bf293230a8be441499a410a309 Binary files /dev/null and b/yolov5/models/__pycache__/experimental.cpython-311.pyc differ diff --git a/yolov5/models/common.py b/yolov5/models/common.py new file mode 100644 index 0000000000000000000000000000000000000000..049dfc0b9e008baa16c8c96ee40fb4819cc6842a --- /dev/null +++ b/yolov5/models/common.py @@ -0,0 +1,1083 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Common modules.""" + +import ast +import contextlib +import json +import math +import platform +import warnings +import zipfile +from collections import OrderedDict, namedtuple +from copy import copy +from pathlib import Path +from urllib.parse import urlparse + +import cv2 +import numpy as np +import pandas as pd +import requests +import torch +import torch.nn as nn +from PIL import Image +from torch.cuda import amp + +# Import 'ultralytics' package or install if missing +try: + import ultralytics + + assert hasattr(ultralytics, "__version__") # verify package is not directory +except (ImportError, AssertionError): + import os + + os.system("pip install -U ultralytics") + import ultralytics + +from ultralytics.utils.plotting import Annotator, colors, save_one_box + +from utils import TryExcept +from utils.dataloaders import exif_transpose, letterbox +from utils.general import ( + LOGGER, + ROOT, + Profile, + check_requirements, + check_suffix, + check_version, + colorstr, + increment_path, + is_jupyter, + make_divisible, + non_max_suppression, + scale_boxes, + xywh2xyxy, + xyxy2xywh, + yaml_load, +) +from utils.torch_utils import copy_attr, smart_inference_mode + + +def autopad(k, p=None, d=1): + """ + Pads kernel to 'same' output shape, adjusting for optional dilation; returns padding size. + + `k`: kernel, `p`: padding, `d`: dilation. + """ + if d > 1: + k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size + if p is None: + p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad + return p + + +class Conv(nn.Module): + # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation) + default_act = nn.SiLU() # default activation + + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True): + """Initializes a standard convolution layer with optional batch normalization and activation.""" + super().__init__() + self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False) + self.bn = nn.BatchNorm2d(c2) + self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity() + + def forward(self, x): + """Applies a convolution followed by batch normalization and an activation function to the input tensor `x`.""" + return self.act(self.bn(self.conv(x))) + + def forward_fuse(self, x): + """Applies a fused convolution and activation function to the input tensor `x`.""" + return self.act(self.conv(x)) + + +class DWConv(Conv): + # Depth-wise convolution + def __init__(self, c1, c2, k=1, s=1, d=1, act=True): + """Initializes a depth-wise convolution layer with optional activation; args: input channels (c1), output + channels (c2), kernel size (k), stride (s), dilation (d), and activation flag (act). + """ + super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act) + + +class DWConvTranspose2d(nn.ConvTranspose2d): + # Depth-wise transpose convolution + def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0): + """Initializes a depth-wise transpose convolutional layer for YOLOv5; args: input channels (c1), output channels + (c2), kernel size (k), stride (s), input padding (p1), output padding (p2). + """ + super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2)) + + +class TransformerLayer(nn.Module): + # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) + def __init__(self, c, num_heads): + """ + Initializes a transformer layer, sans LayerNorm for performance, with multihead attention and linear layers. + + See as described in https://arxiv.org/abs/2010.11929. + """ + super().__init__() + self.q = nn.Linear(c, c, bias=False) + self.k = nn.Linear(c, c, bias=False) + self.v = nn.Linear(c, c, bias=False) + self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) + self.fc1 = nn.Linear(c, c, bias=False) + self.fc2 = nn.Linear(c, c, bias=False) + + def forward(self, x): + """Performs forward pass using MultiheadAttention and two linear transformations with residual connections.""" + x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x + x = self.fc2(self.fc1(x)) + x + return x + + +class TransformerBlock(nn.Module): + # Vision Transformer https://arxiv.org/abs/2010.11929 + def __init__(self, c1, c2, num_heads, num_layers): + """Initializes a Transformer block for vision tasks, adapting dimensions if necessary and stacking specified + layers. + """ + super().__init__() + self.conv = None + if c1 != c2: + self.conv = Conv(c1, c2) + self.linear = nn.Linear(c2, c2) # learnable position embedding + self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers))) + self.c2 = c2 + + def forward(self, x): + """Processes input through an optional convolution, followed by Transformer layers and position embeddings for + object detection. + """ + if self.conv is not None: + x = self.conv(x) + b, _, w, h = x.shape + p = x.flatten(2).permute(2, 0, 1) + return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h) + + +class Bottleneck(nn.Module): + # Standard bottleneck + def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): + """Initializes a standard bottleneck layer with optional shortcut and group convolution, supporting channel + expansion. + """ + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c2, 3, 1, g=g) + self.add = shortcut and c1 == c2 + + def forward(self, x): + """Processes input through two convolutions, optionally adds shortcut if channel dimensions match; input is a + tensor. + """ + 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): + """Initializes CSP bottleneck with optional shortcuts; args: ch_in, ch_out, number of repeats, shortcut bool, + groups, expansion. + """ + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) + self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) + self.cv4 = Conv(2 * c_, c2, 1, 1) + self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) + self.act = nn.SiLU() + self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) + + def forward(self, x): + """Performs forward pass by applying layers, activation, and concatenation on input x, returning feature- + enhanced output. + """ + y1 = self.cv3(self.m(self.cv1(x))) + y2 = self.cv2(x) + return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1)))) + + +class CrossConv(nn.Module): + # Cross Convolution Downsample + def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): + """ + Initializes CrossConv with downsampling, expanding, and optionally shortcutting; `c1` input, `c2` output + channels. + + Inputs are ch_in, ch_out, kernel, stride, groups, expansion, shortcut. + """ + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, (1, k), (1, s)) + self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) + self.add = shortcut and c1 == c2 + + def forward(self, x): + """Performs feature sampling, expanding, and applies shortcut if channels match; expects `x` input tensor.""" + return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) + + +class C3(nn.Module): + # CSP Bottleneck with 3 convolutions + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + """Initializes C3 module with options for channel count, bottleneck repetition, shortcut usage, group + convolutions, and expansion. + """ + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2) + self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) + + def forward(self, x): + """Performs forward propagation using concatenated outputs from two convolutions and a Bottleneck sequence.""" + return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)) + + +class C3x(C3): + # C3 module with cross-convolutions + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + """Initializes C3x module with cross-convolutions, extending C3 with customizable channel dimensions, groups, + and expansion. + """ + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) + self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n))) + + +class C3TR(C3): + # C3 module with TransformerBlock() + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + """Initializes C3 module with TransformerBlock for enhanced feature extraction, accepts channel sizes, shortcut + config, group, and expansion. + """ + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) + self.m = TransformerBlock(c_, c_, 4, n) + + +class C3SPP(C3): + # C3 module with SPP() + def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5): + """Initializes a C3 module with SPP layer for advanced spatial feature extraction, given channel sizes, kernel + sizes, shortcut, group, and expansion ratio. + """ + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) + self.m = SPP(c_, c_, k) + + +class C3Ghost(C3): + # C3 module with GhostBottleneck() + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + """Initializes YOLOv5's C3 module with Ghost Bottlenecks for efficient feature extraction.""" + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n))) + + +class SPP(nn.Module): + # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729 + def __init__(self, c1, c2, k=(5, 9, 13)): + """Initializes SPP layer with Spatial Pyramid Pooling, ref: https://arxiv.org/abs/1406.4729, args: c1 (input channels), c2 (output channels), k (kernel sizes).""" + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) + self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) + + def forward(self, x): + """Applies convolution and max pooling layers to the input tensor `x`, concatenates results, and returns output + tensor. + """ + x = self.cv1(x) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") # suppress torch 1.9.0 max_pool2d() warning + return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) + + +class SPPF(nn.Module): + # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher + def __init__(self, c1, c2, k=5): + """ + Initializes YOLOv5 SPPF layer with given channels and kernel size for YOLOv5 model, combining convolution and + max pooling. + + Equivalent to SPP(k=(5, 9, 13)). + """ + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_ * 4, c2, 1, 1) + self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) + + def forward(self, x): + """Processes input through a series of convolutions and max pooling operations for feature extraction.""" + x = self.cv1(x) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") # suppress torch 1.9.0 max_pool2d() warning + y1 = self.m(x) + y2 = self.m(y1) + return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1)) + + +class Focus(nn.Module): + # Focus wh information into c-space + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): + """Initializes Focus module to concentrate width-height info into channel space with configurable convolution + parameters. + """ + super().__init__() + self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act) + # self.contract = Contract(gain=2) + + def forward(self, x): + """Processes input through Focus mechanism, reshaping (b,c,w,h) to (b,4c,w/2,h/2) then applies convolution.""" + return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1)) + # return self.conv(self.contract(x)) + + +class GhostConv(nn.Module): + # Ghost Convolution https://github.com/huawei-noah/ghostnet + def __init__(self, c1, c2, k=1, s=1, g=1, act=True): + """Initializes GhostConv with in/out channels, kernel size, stride, groups, and activation; halves out channels + for efficiency. + """ + super().__init__() + c_ = c2 // 2 # hidden channels + self.cv1 = Conv(c1, c_, k, s, None, g, act=act) + self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act) + + def forward(self, x): + """Performs forward pass, concatenating outputs of two convolutions on input `x`: shape (B,C,H,W).""" + 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=3, s=1): + """Initializes GhostBottleneck with ch_in `c1`, ch_out `c2`, kernel size `k`, stride `s`; see https://github.com/huawei-noah/ghostnet.""" + super().__init__() + c_ = c2 // 2 + self.conv = nn.Sequential( + GhostConv(c1, c_, 1, 1), # pw + DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw + GhostConv(c_, c2, 1, 1, act=False), + ) # pw-linear + self.shortcut = ( + nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() + ) + + def forward(self, x): + """Processes input through conv and shortcut layers, returning their summed output.""" + return self.conv(x) + self.shortcut(x) + + +class Contract(nn.Module): + # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40) + def __init__(self, gain=2): + """Initializes a layer to contract spatial dimensions (width-height) into channels, e.g., input shape + (1,64,80,80) to (1,256,40,40). + """ + super().__init__() + self.gain = gain + + def forward(self, x): + """Processes input tensor to expand channel dimensions by contracting spatial dimensions, yielding output shape + `(b, c*s*s, h//s, w//s)`. + """ + b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain' + s = self.gain + x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2) + x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) + return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40) + + +class Expand(nn.Module): + # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160) + def __init__(self, gain=2): + """ + Initializes the Expand module to increase spatial dimensions by redistributing channels, with an optional gain + factor. + + Example: x(1,64,80,80) to x(1,16,160,160). + """ + super().__init__() + self.gain = gain + + def forward(self, x): + """Processes input tensor x to expand spatial dimensions by redistributing channels, requiring C / gain^2 == + 0. + """ + b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' + s = self.gain + x = x.view(b, s, s, c // s**2, h, w) # x(1,2,2,16,80,80) + x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) + return x.view(b, c // s**2, h * s, w * s) # x(1,16,160,160) + + +class Concat(nn.Module): + # Concatenate a list of tensors along dimension + def __init__(self, dimension=1): + """Initializes a Concat module to concatenate tensors along a specified dimension.""" + super().__init__() + self.d = dimension + + def forward(self, x): + """Concatenates a list of tensors along a specified dimension; `x` is a list of tensors, `dimension` is an + int. + """ + return torch.cat(x, self.d) + + +class DetectMultiBackend(nn.Module): + # YOLOv5 MultiBackend class for python inference on various backends + def __init__(self, weights="yolov5s.pt", device=torch.device("cpu"), dnn=False, data=None, fp16=False, fuse=True): + """Initializes DetectMultiBackend with support for various inference backends, including PyTorch and ONNX.""" + # PyTorch: weights = *.pt + # TorchScript: *.torchscript + # ONNX Runtime: *.onnx + # ONNX OpenCV DNN: *.onnx --dnn + # OpenVINO: *_openvino_model + # CoreML: *.mlmodel + # TensorRT: *.engine + # TensorFlow SavedModel: *_saved_model + # TensorFlow GraphDef: *.pb + # TensorFlow Lite: *.tflite + # TensorFlow Edge TPU: *_edgetpu.tflite + # PaddlePaddle: *_paddle_model + from models.experimental import attempt_download, attempt_load # scoped to avoid circular import + + super().__init__() + w = str(weights[0] if isinstance(weights, list) else weights) + pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w) + fp16 &= pt or jit or onnx or engine or triton # FP16 + nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH) + stride = 32 # default stride + cuda = torch.cuda.is_available() and device.type != "cpu" # use CUDA + if not (pt or triton): + w = attempt_download(w) # download if not local + + if pt: # PyTorch + model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse) + stride = max(int(model.stride.max()), 32) # model stride + names = model.module.names if hasattr(model, "module") else model.names # get class names + model.half() if fp16 else model.float() + self.model = model # explicitly assign for to(), cpu(), cuda(), half() + elif jit: # TorchScript + LOGGER.info(f"Loading {w} for TorchScript inference...") + extra_files = {"config.txt": ""} # model metadata + model = torch.jit.load(w, _extra_files=extra_files, map_location=device) + model.half() if fp16 else model.float() + if extra_files["config.txt"]: # load metadata dict + d = json.loads( + extra_files["config.txt"], + object_hook=lambda d: {int(k) if k.isdigit() else k: v for k, v in d.items()}, + ) + stride, names = int(d["stride"]), d["names"] + elif dnn: # ONNX OpenCV DNN + LOGGER.info(f"Loading {w} for ONNX OpenCV DNN inference...") + check_requirements("opencv-python>=4.5.4") + net = cv2.dnn.readNetFromONNX(w) + elif onnx: # ONNX Runtime + LOGGER.info(f"Loading {w} for ONNX Runtime inference...") + check_requirements(("onnx", "onnxruntime-gpu" if cuda else "onnxruntime")) + import onnxruntime + + providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] if cuda else ["CPUExecutionProvider"] + session = onnxruntime.InferenceSession(w, providers=providers) + output_names = [x.name for x in session.get_outputs()] + meta = session.get_modelmeta().custom_metadata_map # metadata + if "stride" in meta: + stride, names = int(meta["stride"]), eval(meta["names"]) + elif xml: # OpenVINO + LOGGER.info(f"Loading {w} for OpenVINO inference...") + check_requirements("openvino>=2023.0") # requires openvino-dev: https://pypi.org/project/openvino-dev/ + from openvino.runtime import Core, Layout, get_batch + + core = Core() + if not Path(w).is_file(): # if not *.xml + w = next(Path(w).glob("*.xml")) # get *.xml file from *_openvino_model dir + ov_model = core.read_model(model=w, weights=Path(w).with_suffix(".bin")) + if ov_model.get_parameters()[0].get_layout().empty: + ov_model.get_parameters()[0].set_layout(Layout("NCHW")) + batch_dim = get_batch(ov_model) + if batch_dim.is_static: + batch_size = batch_dim.get_length() + ov_compiled_model = core.compile_model(ov_model, device_name="AUTO") # AUTO selects best available device + stride, names = self._load_metadata(Path(w).with_suffix(".yaml")) # load metadata + elif engine: # TensorRT + LOGGER.info(f"Loading {w} for TensorRT inference...") + import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download + + check_version(trt.__version__, "7.0.0", hard=True) # require tensorrt>=7.0.0 + if device.type == "cpu": + device = torch.device("cuda:0") + Binding = namedtuple("Binding", ("name", "dtype", "shape", "data", "ptr")) + logger = trt.Logger(trt.Logger.INFO) + with open(w, "rb") as f, trt.Runtime(logger) as runtime: + model = runtime.deserialize_cuda_engine(f.read()) + context = model.create_execution_context() + bindings = OrderedDict() + output_names = [] + fp16 = False # default updated below + dynamic = False + is_trt10 = not hasattr(model, "num_bindings") + num = range(model.num_io_tensors) if is_trt10 else range(model.num_bindings) + for i in num: + if is_trt10: + name = model.get_tensor_name(i) + dtype = trt.nptype(model.get_tensor_dtype(name)) + is_input = model.get_tensor_mode(name) == trt.TensorIOMode.INPUT + if is_input: + if -1 in tuple(model.get_tensor_shape(name)): # dynamic + dynamic = True + context.set_input_shape(name, tuple(model.get_profile_shape(name, 0)[2])) + if dtype == np.float16: + fp16 = True + else: # output + output_names.append(name) + shape = tuple(context.get_tensor_shape(name)) + else: + name = model.get_binding_name(i) + dtype = trt.nptype(model.get_binding_dtype(i)) + if model.binding_is_input(i): + if -1 in tuple(model.get_binding_shape(i)): # dynamic + dynamic = True + context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2])) + if dtype == np.float16: + fp16 = True + else: # output + output_names.append(name) + shape = tuple(context.get_binding_shape(i)) + im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device) + bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr())) + binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items()) + batch_size = bindings["images"].shape[0] # if dynamic, this is instead max batch size + elif coreml: # CoreML + LOGGER.info(f"Loading {w} for CoreML inference...") + import coremltools as ct + + model = ct.models.MLModel(w) + elif saved_model: # TF SavedModel + LOGGER.info(f"Loading {w} for TensorFlow SavedModel inference...") + import tensorflow as tf + + keras = False # assume TF1 saved_model + model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w) + elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt + LOGGER.info(f"Loading {w} for TensorFlow GraphDef inference...") + import tensorflow as tf + + def wrap_frozen_graph(gd, inputs, outputs): + """Wraps a TensorFlow GraphDef for inference, returning a pruned function.""" + x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped + ge = x.graph.as_graph_element + return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs)) + + def gd_outputs(gd): + """Generates a sorted list of graph outputs excluding NoOp nodes and inputs, formatted as ':0'.""" + name_list, input_list = [], [] + for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef + name_list.append(node.name) + input_list.extend(node.input) + return sorted(f"{x}:0" for x in list(set(name_list) - set(input_list)) if not x.startswith("NoOp")) + + gd = tf.Graph().as_graph_def() # TF GraphDef + with open(w, "rb") as f: + gd.ParseFromString(f.read()) + frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd)) + elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python + try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu + from tflite_runtime.interpreter import Interpreter, load_delegate + except ImportError: + import tensorflow as tf + + Interpreter, load_delegate = ( + tf.lite.Interpreter, + tf.lite.experimental.load_delegate, + ) + if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime + LOGGER.info(f"Loading {w} for TensorFlow Lite Edge TPU inference...") + delegate = {"Linux": "libedgetpu.so.1", "Darwin": "libedgetpu.1.dylib", "Windows": "edgetpu.dll"}[ + platform.system() + ] + interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)]) + else: # TFLite + LOGGER.info(f"Loading {w} for TensorFlow Lite inference...") + interpreter = Interpreter(model_path=w) # load TFLite model + interpreter.allocate_tensors() # allocate + input_details = interpreter.get_input_details() # inputs + output_details = interpreter.get_output_details() # outputs + # load metadata + with contextlib.suppress(zipfile.BadZipFile): + with zipfile.ZipFile(w, "r") as model: + meta_file = model.namelist()[0] + meta = ast.literal_eval(model.read(meta_file).decode("utf-8")) + stride, names = int(meta["stride"]), meta["names"] + elif tfjs: # TF.js + raise NotImplementedError("ERROR: YOLOv5 TF.js inference is not supported") + elif paddle: # PaddlePaddle + LOGGER.info(f"Loading {w} for PaddlePaddle inference...") + check_requirements("paddlepaddle-gpu" if cuda else "paddlepaddle") + import paddle.inference as pdi + + if not Path(w).is_file(): # if not *.pdmodel + w = next(Path(w).rglob("*.pdmodel")) # get *.pdmodel file from *_paddle_model dir + weights = Path(w).with_suffix(".pdiparams") + config = pdi.Config(str(w), str(weights)) + if cuda: + config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0) + predictor = pdi.create_predictor(config) + input_handle = predictor.get_input_handle(predictor.get_input_names()[0]) + output_names = predictor.get_output_names() + elif triton: # NVIDIA Triton Inference Server + LOGGER.info(f"Using {w} as Triton Inference Server...") + check_requirements("tritonclient[all]") + from utils.triton import TritonRemoteModel + + model = TritonRemoteModel(url=w) + nhwc = model.runtime.startswith("tensorflow") + else: + raise NotImplementedError(f"ERROR: {w} is not a supported format") + + # class names + if "names" not in locals(): + names = yaml_load(data)["names"] if data else {i: f"class{i}" for i in range(999)} + if names[0] == "n01440764" and len(names) == 1000: # ImageNet + names = yaml_load(ROOT / "data/ImageNet.yaml")["names"] # human-readable names + + self.__dict__.update(locals()) # assign all variables to self + + def forward(self, im, augment=False, visualize=False): + """Performs YOLOv5 inference on input images with options for augmentation and visualization.""" + b, ch, h, w = im.shape # batch, channel, height, width + if self.fp16 and im.dtype != torch.float16: + im = im.half() # to FP16 + if self.nhwc: + im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3) + + if self.pt: # PyTorch + y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im) + elif self.jit: # TorchScript + y = self.model(im) + elif self.dnn: # ONNX OpenCV DNN + im = im.cpu().numpy() # torch to numpy + self.net.setInput(im) + y = self.net.forward() + elif self.onnx: # ONNX Runtime + im = im.cpu().numpy() # torch to numpy + y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im}) + elif self.xml: # OpenVINO + im = im.cpu().numpy() # FP32 + y = list(self.ov_compiled_model(im).values()) + elif self.engine: # TensorRT + if self.dynamic and im.shape != self.bindings["images"].shape: + i = self.model.get_binding_index("images") + self.context.set_binding_shape(i, im.shape) # reshape if dynamic + self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape) + for name in self.output_names: + i = self.model.get_binding_index(name) + self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i))) + s = self.bindings["images"].shape + assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}" + self.binding_addrs["images"] = int(im.data_ptr()) + self.context.execute_v2(list(self.binding_addrs.values())) + y = [self.bindings[x].data for x in sorted(self.output_names)] + elif self.coreml: # CoreML + im = im.cpu().numpy() + im = Image.fromarray((im[0] * 255).astype("uint8")) + # im = im.resize((192, 320), Image.BILINEAR) + y = self.model.predict({"image": im}) # coordinates are xywh normalized + if "confidence" in y: + box = xywh2xyxy(y["coordinates"] * [[w, h, w, h]]) # xyxy pixels + conf, cls = y["confidence"].max(1), y["confidence"].argmax(1).astype(np.float) + y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1) + else: + y = list(reversed(y.values())) # reversed for segmentation models (pred, proto) + elif self.paddle: # PaddlePaddle + im = im.cpu().numpy().astype(np.float32) + self.input_handle.copy_from_cpu(im) + self.predictor.run() + y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names] + elif self.triton: # NVIDIA Triton Inference Server + y = self.model(im) + else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU) + im = im.cpu().numpy() + if self.saved_model: # SavedModel + y = self.model(im, training=False) if self.keras else self.model(im) + elif self.pb: # GraphDef + y = self.frozen_func(x=self.tf.constant(im)) + else: # Lite or Edge TPU + input = self.input_details[0] + int8 = input["dtype"] == np.uint8 # is TFLite quantized uint8 model + if int8: + scale, zero_point = input["quantization"] + im = (im / scale + zero_point).astype(np.uint8) # de-scale + self.interpreter.set_tensor(input["index"], im) + self.interpreter.invoke() + y = [] + for output in self.output_details: + x = self.interpreter.get_tensor(output["index"]) + if int8: + scale, zero_point = output["quantization"] + x = (x.astype(np.float32) - zero_point) * scale # re-scale + y.append(x) + y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y] + y[0][..., :4] *= [w, h, w, h] # xywh normalized to pixels + + if isinstance(y, (list, tuple)): + return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y] + else: + return self.from_numpy(y) + + def from_numpy(self, x): + """Converts a NumPy array to a torch tensor, maintaining device compatibility.""" + return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x + + def warmup(self, imgsz=(1, 3, 640, 640)): + """Performs a single inference warmup to initialize model weights, accepting an `imgsz` tuple for image size.""" + warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton + if any(warmup_types) and (self.device.type != "cpu" or self.triton): + im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input + for _ in range(2 if self.jit else 1): # + self.forward(im) # warmup + + @staticmethod + def _model_type(p="path/to/model.pt"): + """ + Determines model type from file path or URL, supporting various export formats. + + Example: path='path/to/model.onnx' -> type=onnx + """ + # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle] + from export import export_formats + from utils.downloads import is_url + + sf = list(export_formats().Suffix) # export suffixes + if not is_url(p, check=False): + check_suffix(p, sf) # checks + url = urlparse(p) # if url may be Triton inference server + types = [s in Path(p).name for s in sf] + types[8] &= not types[9] # tflite &= not edgetpu + triton = not any(types) and all([any(s in url.scheme for s in ["http", "grpc"]), url.netloc]) + return types + [triton] + + @staticmethod + def _load_metadata(f=Path("path/to/meta.yaml")): + """Loads metadata from a YAML file, returning strides and names if the file exists, otherwise `None`.""" + if f.exists(): + d = yaml_load(f) + return d["stride"], d["names"] # assign stride, names + return None, None + + +class AutoShape(nn.Module): + # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS + conf = 0.25 # NMS confidence threshold + iou = 0.45 # NMS IoU threshold + agnostic = False # NMS class-agnostic + multi_label = False # NMS multiple labels per box + classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs + max_det = 1000 # maximum number of detections per image + amp = False # Automatic Mixed Precision (AMP) inference + + def __init__(self, model, verbose=True): + """Initializes YOLOv5 model for inference, setting up attributes and preparing model for evaluation.""" + super().__init__() + if verbose: + LOGGER.info("Adding AutoShape... ") + copy_attr(self, model, include=("yaml", "nc", "hyp", "names", "stride", "abc"), exclude=()) # copy attributes + self.dmb = isinstance(model, DetectMultiBackend) # DetectMultiBackend() instance + self.pt = not self.dmb or model.pt # PyTorch model + self.model = model.eval() + if self.pt: + m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() + m.inplace = False # Detect.inplace=False for safe multithread inference + m.export = True # do not output loss values + + def _apply(self, fn): + """ + Applies to(), cpu(), cuda(), half() etc. + + to model tensors excluding parameters or registered buffers. + """ + self = super()._apply(fn) + if self.pt: + m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() + m.stride = fn(m.stride) + m.grid = list(map(fn, m.grid)) + if isinstance(m.anchor_grid, list): + m.anchor_grid = list(map(fn, m.anchor_grid)) + return self + + @smart_inference_mode() + def forward(self, ims, size=640, augment=False, profile=False): + """ + Performs inference on inputs with optional augment & profiling. + + Supports various formats including file, URI, OpenCV, PIL, numpy, torch. + """ + # For size(height=640, width=1280), RGB images example inputs are: + # file: ims = 'data/images/zidane.jpg' # str or PosixPath + # URI: = 'https://ultralytics.com/images/zidane.jpg' + # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) + # PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3) + # numpy: = np.zeros((640,1280,3)) # HWC + # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) + # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images + + dt = (Profile(), Profile(), Profile()) + with dt[0]: + if isinstance(size, int): # expand + size = (size, size) + p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param + autocast = self.amp and (p.device.type != "cpu") # Automatic Mixed Precision (AMP) inference + if isinstance(ims, torch.Tensor): # torch + with amp.autocast(autocast): + return self.model(ims.to(p.device).type_as(p), augment=augment) # inference + + # Pre-process + n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images + shape0, shape1, files = [], [], [] # image and inference shapes, filenames + for i, im in enumerate(ims): + f = f"image{i}" # filename + if isinstance(im, (str, Path)): # filename or uri + im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith("http") else im), im + im = np.asarray(exif_transpose(im)) + elif isinstance(im, Image.Image): # PIL Image + im, f = np.asarray(exif_transpose(im)), getattr(im, "filename", f) or f + files.append(Path(f).with_suffix(".jpg").name) + if im.shape[0] < 5: # image in CHW + im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) + im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input + s = im.shape[:2] # HWC + shape0.append(s) # image shape + g = max(size) / max(s) # gain + shape1.append([int(y * g) for y in s]) + ims[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update + shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] # inf shape + x = [letterbox(im, shape1, auto=False)[0] for im in ims] # pad + x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW + x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32 + + with amp.autocast(autocast): + # Inference + with dt[1]: + y = self.model(x, augment=augment) # forward + + # Post-process + with dt[2]: + y = non_max_suppression( + y if self.dmb else y[0], + self.conf, + self.iou, + self.classes, + self.agnostic, + self.multi_label, + max_det=self.max_det, + ) # NMS + for i in range(n): + scale_boxes(shape1, y[i][:, :4], shape0[i]) + + return Detections(ims, y, files, dt, self.names, x.shape) + + +class Detections: + # YOLOv5 detections class for inference results + def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None): + """Initializes the YOLOv5 Detections class with image info, predictions, filenames, timing and normalization.""" + super().__init__() + d = pred[0].device # device + gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims] # normalizations + self.ims = ims # list of images as numpy arrays + self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) + self.names = names # class names + self.files = files # image filenames + self.times = times # profiling times + self.xyxy = pred # xyxy pixels + self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels + self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized + self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized + self.n = len(self.pred) # number of images (batch size) + self.t = tuple(x.t / self.n * 1e3 for x in times) # timestamps (ms) + self.s = tuple(shape) # inference BCHW shape + + def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path("")): + """Executes model predictions, displaying and/or saving outputs with optional crops and labels.""" + s, crops = "", [] + for i, (im, pred) in enumerate(zip(self.ims, self.pred)): + s += f"\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} " # string + if pred.shape[0]: + for c in pred[:, -1].unique(): + n = (pred[:, -1] == c).sum() # detections per class + s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string + s = s.rstrip(", ") + if show or save or render or crop: + annotator = Annotator(im, example=str(self.names)) + for *box, conf, cls in reversed(pred): # xyxy, confidence, class + label = f"{self.names[int(cls)]} {conf:.2f}" + if crop: + file = save_dir / "crops" / self.names[int(cls)] / self.files[i] if save else None + crops.append( + { + "box": box, + "conf": conf, + "cls": cls, + "label": label, + "im": save_one_box(box, im, file=file, save=save), + } + ) + else: # all others + annotator.box_label(box, label if labels else "", color=colors(cls)) + im = annotator.im + else: + s += "(no detections)" + + im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np + if show: + if is_jupyter(): + from IPython.display import display + + display(im) + else: + im.show(self.files[i]) + if save: + f = self.files[i] + im.save(save_dir / f) # save + if i == self.n - 1: + LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}") + if render: + self.ims[i] = np.asarray(im) + if pprint: + s = s.lstrip("\n") + return f"{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}" % self.t + if crop: + if save: + LOGGER.info(f"Saved results to {save_dir}\n") + return crops + + @TryExcept("Showing images is not supported in this environment") + def show(self, labels=True): + """ + Displays detection results with optional labels. + + Usage: show(labels=True) + """ + self._run(show=True, labels=labels) # show results + + def save(self, labels=True, save_dir="runs/detect/exp", exist_ok=False): + """ + Saves detection results with optional labels to a specified directory. + + Usage: save(labels=True, save_dir='runs/detect/exp', exist_ok=False) + """ + save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir + self._run(save=True, labels=labels, save_dir=save_dir) # save results + + def crop(self, save=True, save_dir="runs/detect/exp", exist_ok=False): + """ + Crops detection results, optionally saves them to a directory. + + Args: save (bool), save_dir (str), exist_ok (bool). + """ + save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None + return self._run(crop=True, save=save, save_dir=save_dir) # crop results + + def render(self, labels=True): + """Renders detection results with optional labels on images; args: labels (bool) indicating label inclusion.""" + self._run(render=True, labels=labels) # render results + return self.ims + + def pandas(self): + """ + Returns detections as pandas DataFrames for various box formats (xyxy, xyxyn, xywh, xywhn). + + Example: print(results.pandas().xyxy[0]). + """ + new = copy(self) # return copy + ca = "xmin", "ymin", "xmax", "ymax", "confidence", "class", "name" # xyxy columns + cb = "xcenter", "ycenter", "width", "height", "confidence", "class", "name" # xywh columns + for k, c in zip(["xyxy", "xyxyn", "xywh", "xywhn"], [ca, ca, cb, cb]): + a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update + setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) + return new + + def tolist(self): + """ + Converts a Detections object into a list of individual detection results for iteration. + + Example: for result in results.tolist(): + """ + r = range(self.n) # iterable + return [ + Detections( + [self.ims[i]], + [self.pred[i]], + [self.files[i]], + self.times, + self.names, + self.s, + ) + for i in r + ] + + def print(self): + """Logs the string representation of the current object's state via the LOGGER.""" + LOGGER.info(self.__str__()) + + def __len__(self): + """Returns the number of results stored, overrides the default len(results).""" + return self.n + + def __str__(self): + """Returns a string representation of the model's results, suitable for printing, overrides default + print(results). + """ + return self._run(pprint=True) # print results + + def __repr__(self): + """Returns a string representation of the YOLOv5 object, including its class and formatted results.""" + return f"YOLOv5 {self.__class__} instance\n" + self.__str__() + + +class Proto(nn.Module): + # YOLOv5 mask Proto module for segmentation models + def __init__(self, c1, c_=256, c2=32): + """Initializes YOLOv5 Proto module for segmentation with input, proto, and mask channels configuration.""" + super().__init__() + self.cv1 = Conv(c1, c_, k=3) + self.upsample = nn.Upsample(scale_factor=2, mode="nearest") + self.cv2 = Conv(c_, c_, k=3) + self.cv3 = Conv(c_, c2) + + def forward(self, x): + """Performs a forward pass using convolutional layers and upsampling on input tensor `x`.""" + return self.cv3(self.cv2(self.upsample(self.cv1(x)))) + + +class Classify(nn.Module): + # YOLOv5 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, dropout_p=0.0 + ): # ch_in, ch_out, kernel, stride, padding, groups, dropout probability + """Initializes YOLOv5 classification head with convolution, pooling, and dropout layers for input to output + channel transformation. + """ + super().__init__() + c_ = 1280 # efficientnet_b0 size + self.conv = Conv(c1, c_, k, s, autopad(k, p), g) + self.pool = nn.AdaptiveAvgPool2d(1) # to x(b,c_,1,1) + self.drop = nn.Dropout(p=dropout_p, inplace=True) + self.linear = nn.Linear(c_, c2) # to x(b,c2) + + def forward(self, x): + """Processes input through conv, pool, drop, and linear layers; supports list concatenation input.""" + if isinstance(x, list): + x = torch.cat(x, 1) + return self.linear(self.drop(self.pool(self.conv(x)).flatten(1))) diff --git a/yolov5/models/experimental.py b/yolov5/models/experimental.py new file mode 100644 index 0000000000000000000000000000000000000000..ab9b0ed23dc3fd5e40acd8919450f9d0cf7a134e --- /dev/null +++ b/yolov5/models/experimental.py @@ -0,0 +1,130 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Experimental modules.""" + +import math + +import numpy as np +import torch +import torch.nn as nn + +from utils.downloads import attempt_download + + +class Sum(nn.Module): + """Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070.""" + + def __init__(self, n, weight=False): + """Initializes a module to sum outputs of layers with number of inputs `n` and optional weighting, supporting 2+ + inputs. + """ + super().__init__() + self.weight = weight # apply weights boolean + self.iter = range(n - 1) # iter object + if weight: + self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True) # layer weights + + def forward(self, x): + """Processes input through a customizable weighted sum of `n` inputs, optionally applying learned weights.""" + y = x[0] # no weight + if self.weight: + w = torch.sigmoid(self.w) * 2 + for i in self.iter: + y = y + x[i + 1] * w[i] + else: + for i in self.iter: + y = y + x[i + 1] + return y + + +class MixConv2d(nn.Module): + """Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595.""" + + def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): + """Initializes MixConv2d with mixed depth-wise convolutional layers, taking input and output channels (c1, c2), + kernel sizes (k), stride (s), and channel distribution strategy (equal_ch). + """ + super().__init__() + n = len(k) # number of convolutions + if equal_ch: # equal c_ per group + i = torch.linspace(0, n - 1e-6, c2).floor() # c2 indices + c_ = [(i == g).sum() for g in range(n)] # intermediate channels + else: # equal weight.numel() per group + b = [c2] + [0] * n + a = np.eye(n + 1, n, k=-1) + a -= np.roll(a, 1, axis=1) + a *= np.array(k) ** 2 + a[0] = 1 + c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b + + self.m = nn.ModuleList( + [nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)] + ) + self.bn = nn.BatchNorm2d(c2) + self.act = nn.SiLU() + + def forward(self, x): + """Performs forward pass by applying SiLU activation on batch-normalized concatenated convolutional layer + outputs. + """ + return self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) + + +class Ensemble(nn.ModuleList): + """Ensemble of models.""" + + def __init__(self): + """Initializes an ensemble of models to be used for aggregated predictions.""" + super().__init__() + + def forward(self, x, augment=False, profile=False, visualize=False): + """Performs forward pass aggregating outputs from an ensemble of models..""" + y = [module(x, augment, profile, visualize)[0] for module in self] + # y = torch.stack(y).max(0)[0] # max ensemble + # y = torch.stack(y).mean(0) # mean ensemble + y = torch.cat(y, 1) # nms ensemble + return y, None # inference, train output + + +def attempt_load(weights, device=None, inplace=True, fuse=True): + """ + Loads and fuses an ensemble or single YOLOv5 model from weights, handling device placement and model adjustments. + + Example inputs: weights=[a,b,c] or a single model weights=[a] or weights=a. + """ + from models.yolo import Detect, Model + + model = Ensemble() + for w in weights if isinstance(weights, list) else [weights]: + ckpt = torch.load(attempt_download(w), map_location="cpu") # load + ckpt = (ckpt.get("ema") or ckpt["model"]).to(device).float() # FP32 model + + # Model compatibility updates + if not hasattr(ckpt, "stride"): + ckpt.stride = torch.tensor([32.0]) + if hasattr(ckpt, "names") and isinstance(ckpt.names, (list, tuple)): + ckpt.names = dict(enumerate(ckpt.names)) # convert to dict + + model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, "fuse") else ckpt.eval()) # model in eval mode + + # Module updates + for m in model.modules(): + t = type(m) + if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model): + m.inplace = inplace + if t is Detect and not isinstance(m.anchor_grid, list): + delattr(m, "anchor_grid") + setattr(m, "anchor_grid", [torch.zeros(1)] * m.nl) + elif t is nn.Upsample and not hasattr(m, "recompute_scale_factor"): + m.recompute_scale_factor = None # torch 1.11.0 compatibility + + # Return model + if len(model) == 1: + return model[-1] + + # Return detection ensemble + print(f"Ensemble created with {weights}\n") + for k in "names", "nc", "yaml": + setattr(model, k, getattr(model[0], k)) + model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride + assert all(model[0].nc == m.nc for m in model), f"Models have different class counts: {[m.nc for m in model]}" + return model diff --git a/yolov5/models/hub/anchors.yaml b/yolov5/models/hub/anchors.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c8089311b267c5845b01856f32686e7392ba76c5 --- /dev/null +++ b/yolov5/models/hub/anchors.yaml @@ -0,0 +1,56 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# Default anchors for COCO data + +# P5 ------------------------------------------------------------------------------------------------------------------- +# P5-640: +anchors_p5_640: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# P6 ------------------------------------------------------------------------------------------------------------------- +# P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387 +anchors_p6_640: + - [9, 11, 21, 19, 17, 41] # P3/8 + - [43, 32, 39, 70, 86, 64] # P4/16 + - [65, 131, 134, 130, 120, 265] # P5/32 + - [282, 180, 247, 354, 512, 387] # P6/64 + +# P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792 +anchors_p6_1280: + - [19, 27, 44, 40, 38, 94] # P3/8 + - [96, 68, 86, 152, 180, 137] # P4/16 + - [140, 301, 303, 264, 238, 542] # P5/32 + - [436, 615, 739, 380, 925, 792] # P6/64 + +# P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187 +anchors_p6_1920: + - [28, 41, 67, 59, 57, 141] # P3/8 + - [144, 103, 129, 227, 270, 205] # P4/16 + - [209, 452, 455, 396, 358, 812] # P5/32 + - [653, 922, 1109, 570, 1387, 1187] # P6/64 + +# P7 ------------------------------------------------------------------------------------------------------------------- +# P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372 +anchors_p7_640: + - [11, 11, 13, 30, 29, 20] # P3/8 + - [30, 46, 61, 38, 39, 92] # P4/16 + - [78, 80, 146, 66, 79, 163] # P5/32 + - [149, 150, 321, 143, 157, 303] # P6/64 + - [257, 402, 359, 290, 524, 372] # P7/128 + +# P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818 +anchors_p7_1280: + - [19, 22, 54, 36, 32, 77] # P3/8 + - [70, 83, 138, 71, 75, 173] # P4/16 + - [165, 159, 148, 334, 375, 151] # P5/32 + - [334, 317, 251, 626, 499, 474] # P6/64 + - [750, 326, 534, 814, 1079, 818] # P7/128 + +# P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227 +anchors_p7_1920: + - [29, 34, 81, 55, 47, 115] # P3/8 + - [105, 124, 207, 107, 113, 259] # P4/16 + - [247, 238, 222, 500, 563, 227] # P5/32 + - [501, 476, 376, 939, 749, 711] # P6/64 + - [1126, 489, 801, 1222, 1618, 1227] # P7/128 diff --git a/yolov5/models/hub/yolov3-spp.yaml b/yolov5/models/hub/yolov3-spp.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0e073667bf7023f7c8ff2d21816377e8187a3d5a --- /dev/null +++ b/yolov5/models/hub/yolov3-spp.yaml @@ -0,0 +1,52 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# darknet53 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, Bottleneck, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, Bottleneck, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, Bottleneck, [1024]], # 10 + ] + +# YOLOv3-SPP head +head: [ + [-1, 1, Bottleneck, [1024, False]], + [-1, 1, SPP, [512, [5, 9, 13]]], + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Bottleneck, [256, False]], + [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) + + [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/hub/yolov3-tiny.yaml b/yolov5/models/hub/yolov3-tiny.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0a74fff715f85e66c393306b9821ec1ead3c7f66 --- /dev/null +++ b/yolov5/models/hub/yolov3-tiny.yaml @@ -0,0 +1,42 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10, 14, 23, 27, 37, 58] # P4/16 + - [81, 82, 135, 169, 344, 319] # P5/32 + +# YOLOv3-tiny backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [16, 3, 1]], # 0 + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 + [-1, 1, Conv, [32, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 + [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 + ] + +# YOLOv3-tiny head +head: [ + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) + + [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5) + ] diff --git a/yolov5/models/hub/yolov3.yaml b/yolov5/models/hub/yolov3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ce4a980c82008cb00e4a8892c349b2c584a99a58 --- /dev/null +++ b/yolov5/models/hub/yolov3.yaml @@ -0,0 +1,52 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# darknet53 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, Bottleneck, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, Bottleneck, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, Bottleneck, [1024]], # 10 + ] + +# YOLOv3 head +head: [ + [-1, 1, Bottleneck, [1024, False]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Bottleneck, [256, False]], + [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) + + [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/hub/yolov5-bifpn.yaml b/yolov5/models/hub/yolov5-bifpn.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bf05e434c6a2b6a2e83ab3585d4583afd607353e --- /dev/null +++ b/yolov5/models/hub/yolov5-bifpn.yaml @@ -0,0 +1,49 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 BiFPN head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14, 6], 1, Concat, [1]], # cat P4 <--- BiFPN change + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/hub/yolov5-fpn.yaml b/yolov5/models/hub/yolov5-fpn.yaml new file mode 100644 index 0000000000000000000000000000000000000000..dcfdd14a7d2c5691284e096962359898b938fe49 --- /dev/null +++ b/yolov5/models/hub/yolov5-fpn.yaml @@ -0,0 +1,43 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 FPN head +head: [ + [-1, 3, C3, [1024, False]], # 10 (P5/32-large) + + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Conv, [512, 1, 1]], + [-1, 3, C3, [512, False]], # 14 (P4/16-medium) + + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Conv, [256, 1, 1]], + [-1, 3, C3, [256, False]], # 18 (P3/8-small) + + [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/hub/yolov5-p2.yaml b/yolov5/models/hub/yolov5-p2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2626e734835e47f1a5385d06f3a2ee348d39a4d4 --- /dev/null +++ b/yolov5/models/hub/yolov5-p2.yaml @@ -0,0 +1,55 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: 3 # AutoAnchor evolves 3 anchors per P output layer + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head with (P2, P3, P4, P5) outputs +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 2], 1, Concat, [1]], # cat backbone P2 + [-1, 1, C3, [128, False]], # 21 (P2/4-xsmall) + + [-1, 1, Conv, [128, 3, 2]], + [[-1, 18], 1, Concat, [1]], # cat head P3 + [-1, 3, C3, [256, False]], # 24 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 27 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 30 (P5/32-large) + + [[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(P2, P3, P4, P5) + ] diff --git a/yolov5/models/hub/yolov5-p34.yaml b/yolov5/models/hub/yolov5-p34.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fba35ec10a1e9fc562d09210cc91f5dd1269e639 --- /dev/null +++ b/yolov5/models/hub/yolov5-p34.yaml @@ -0,0 +1,42 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: 3 # AutoAnchor evolves 3 anchors per P output layer + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head with (P3, P4) outputs +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [[17, 20], 1, Detect, [nc, anchors]], # Detect(P3, P4) + ] diff --git a/yolov5/models/hub/yolov5-p6.yaml b/yolov5/models/hub/yolov5-p6.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c997df2db505acc5ace837f79d23c9c8780abb38 --- /dev/null +++ b/yolov5/models/hub/yolov5-p6.yaml @@ -0,0 +1,57 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: 3 # AutoAnchor evolves 3 anchors per P output layer + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head with (P3, P4, P5, P6) outputs +head: [ + [-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/yolov5/models/hub/yolov5-p7.yaml b/yolov5/models/hub/yolov5-p7.yaml new file mode 100644 index 0000000000000000000000000000000000000000..14e6ce05d791a8ad23d800db5ab1c9ba823df684 --- /dev/null +++ b/yolov5/models/hub/yolov5-p7.yaml @@ -0,0 +1,68 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: 3 # AutoAnchor evolves 3 anchors per P output layer + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, Conv, [1280, 3, 2]], # 11-P7/128 + [-1, 3, C3, [1280]], + [-1, 1, SPPF, [1280, 5]], # 13 + ] + +# YOLOv5 v6.0 head with (P3, P4, P5, P6, P7) outputs +head: [ + [-1, 1, Conv, [1024, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 10], 1, Concat, [1]], # cat backbone P6 + [-1, 3, C3, [1024, False]], # 17 + + [-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 21 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 25 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 29 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 26], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 32 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 22], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 35 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 18], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 38 (P6/64-xlarge) + + [-1, 1, Conv, [1024, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P7 + [-1, 3, C3, [1280, False]], # 41 (P7/128-xxlarge) + + [[29, 32, 35, 38, 41], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6, P7) + ] diff --git a/yolov5/models/hub/yolov5-panet.yaml b/yolov5/models/hub/yolov5-panet.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f0857f92d53a21e8d3809cf31e4fe58145e85094 --- /dev/null +++ b/yolov5/models/hub/yolov5-panet.yaml @@ -0,0 +1,49 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 PANet head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/hub/yolov5l6.yaml b/yolov5/models/hub/yolov5l6.yaml new file mode 100644 index 0000000000000000000000000000000000000000..05501a9d134b8d03ef25cc5b21530f3dd3cfd458 --- /dev/null +++ b/yolov5/models/hub/yolov5l6.yaml @@ -0,0 +1,61 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [19, 27, 44, 40, 38, 94] # P3/8 + - [96, 68, 86, 152, 180, 137] # P4/16 + - [140, 301, 303, 264, 238, 542] # P5/32 + - [436, 615, 739, 380, 925, 792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/yolov5/models/hub/yolov5m6.yaml b/yolov5/models/hub/yolov5m6.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1512e2b6e08ddabb9f6d494eac1910bfd4527c89 --- /dev/null +++ b/yolov5/models/hub/yolov5m6.yaml @@ -0,0 +1,61 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.67 # model depth multiple +width_multiple: 0.75 # layer channel multiple +anchors: + - [19, 27, 44, 40, 38, 94] # P3/8 + - [96, 68, 86, 152, 180, 137] # P4/16 + - [140, 301, 303, 264, 238, 542] # P5/32 + - [436, 615, 739, 380, 925, 792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/yolov5/models/hub/yolov5n6.yaml b/yolov5/models/hub/yolov5n6.yaml new file mode 100644 index 0000000000000000000000000000000000000000..11350413ecf82575d75a29054260ca85621f91d4 --- /dev/null +++ b/yolov5/models/hub/yolov5n6.yaml @@ -0,0 +1,61 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.25 # layer channel multiple +anchors: + - [19, 27, 44, 40, 38, 94] # P3/8 + - [96, 68, 86, 152, 180, 137] # P4/16 + - [140, 301, 303, 264, 238, 542] # P5/32 + - [436, 615, 739, 380, 925, 792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/yolov5/models/hub/yolov5s-LeakyReLU.yaml b/yolov5/models/hub/yolov5s-LeakyReLU.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6e9d4a8820e2b5fa42fd2ff7c6cd439d42e037a5 --- /dev/null +++ b/yolov5/models/hub/yolov5s-LeakyReLU.yaml @@ -0,0 +1,50 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +activation: nn.LeakyReLU(0.1) # <----- Conv() activation used throughout entire YOLOv5 model +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/hub/yolov5s-ghost.yaml b/yolov5/models/hub/yolov5s-ghost.yaml new file mode 100644 index 0000000000000000000000000000000000000000..cc433694876070576117c0619644f80efad9e6b4 --- /dev/null +++ b/yolov5/models/hub/yolov5s-ghost.yaml @@ -0,0 +1,49 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3Ghost, [128]], + [-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3Ghost, [256]], + [-1, 1, GhostConv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3Ghost, [512]], + [-1, 1, GhostConv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3Ghost, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, GhostConv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3Ghost, [512, False]], # 13 + + [-1, 1, GhostConv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3Ghost, [256, False]], # 17 (P3/8-small) + + [-1, 1, GhostConv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3Ghost, [512, False]], # 20 (P4/16-medium) + + [-1, 1, GhostConv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3Ghost, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/hub/yolov5s-transformer.yaml b/yolov5/models/hub/yolov5s-transformer.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1b2d62c5a3fefc5978000b1e07bbab02574a5f24 --- /dev/null +++ b/yolov5/models/hub/yolov5s-transformer.yaml @@ -0,0 +1,49 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3TR, [1024]], # 9 <--- C3TR() Transformer module + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/hub/yolov5s6.yaml b/yolov5/models/hub/yolov5s6.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2a4c1162575b6b2e17e9383cb6472ddd857cadac --- /dev/null +++ b/yolov5/models/hub/yolov5s6.yaml @@ -0,0 +1,61 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [19, 27, 44, 40, 38, 94] # P3/8 + - [96, 68, 86, 152, 180, 137] # P4/16 + - [140, 301, 303, 264, 238, 542] # P5/32 + - [436, 615, 739, 380, 925, 792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/yolov5/models/hub/yolov5x6.yaml b/yolov5/models/hub/yolov5x6.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0c8f29e600c3f4be4bdd3c14ea4739d3797a517a --- /dev/null +++ b/yolov5/models/hub/yolov5x6.yaml @@ -0,0 +1,61 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.33 # model depth multiple +width_multiple: 1.25 # layer channel multiple +anchors: + - [19, 27, 44, 40, 38, 94] # P3/8 + - [96, 68, 86, 152, 180, 137] # P4/16 + - [140, 301, 303, 264, 238, 542] # P5/32 + - [436, 615, 739, 380, 925, 792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/yolov5/models/segment/yolov5l-seg.yaml b/yolov5/models/segment/yolov5l-seg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..de430f4fbdf3dcba06f74a565a185494617ab22d --- /dev/null +++ b/yolov5/models/segment/yolov5l-seg.yaml @@ -0,0 +1,49 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/segment/yolov5m-seg.yaml b/yolov5/models/segment/yolov5m-seg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..288577778252120d28c6099cf848984222fe39fb --- /dev/null +++ b/yolov5/models/segment/yolov5m-seg.yaml @@ -0,0 +1,49 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.67 # model depth multiple +width_multiple: 0.75 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/segment/yolov5n-seg.yaml b/yolov5/models/segment/yolov5n-seg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..faf5228fd3ef3532b62b145ae74ddbb9fb6f48a8 --- /dev/null +++ b/yolov5/models/segment/yolov5n-seg.yaml @@ -0,0 +1,49 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.25 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/segment/yolov5s-seg.yaml b/yolov5/models/segment/yolov5s-seg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a199f1d823882f863653f1574fb5e45c39caa0e8 --- /dev/null +++ b/yolov5/models/segment/yolov5s-seg.yaml @@ -0,0 +1,49 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.5 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/segment/yolov5x-seg.yaml b/yolov5/models/segment/yolov5x-seg.yaml new file mode 100644 index 0000000000000000000000000000000000000000..75f426386e3a873a1f1b7b0381180f023bc88364 --- /dev/null +++ b/yolov5/models/segment/yolov5x-seg.yaml @@ -0,0 +1,49 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.33 # model depth multiple +width_multiple: 1.25 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/tf.py b/yolov5/models/tf.py new file mode 100644 index 0000000000000000000000000000000000000000..9884ec3db355bc4c3fcd375e800d71ca0b4f7a22 --- /dev/null +++ b/yolov5/models/tf.py @@ -0,0 +1,775 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +""" +TensorFlow, Keras and TFLite versions of YOLOv5 +Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127 + +Usage: + $ python models/tf.py --weights yolov5s.pt + +Export: + $ python export.py --weights yolov5s.pt --include saved_model pb tflite tfjs +""" + +import argparse +import sys +from copy import deepcopy +from pathlib import Path + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +# ROOT = ROOT.relative_to(Path.cwd()) # relative + +import numpy as np +import tensorflow as tf +import torch +import torch.nn as nn +from tensorflow import keras + +from models.common import ( + C3, + SPP, + SPPF, + Bottleneck, + BottleneckCSP, + C3x, + Concat, + Conv, + CrossConv, + DWConv, + DWConvTranspose2d, + Focus, + autopad, +) +from models.experimental import MixConv2d, attempt_load +from models.yolo import Detect, Segment +from utils.activations import SiLU +from utils.general import LOGGER, make_divisible, print_args + + +class TFBN(keras.layers.Layer): + # TensorFlow BatchNormalization wrapper + def __init__(self, w=None): + """Initializes a TensorFlow BatchNormalization layer with optional pretrained weights.""" + super().__init__() + self.bn = keras.layers.BatchNormalization( + beta_initializer=keras.initializers.Constant(w.bias.numpy()), + gamma_initializer=keras.initializers.Constant(w.weight.numpy()), + moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()), + moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy()), + epsilon=w.eps, + ) + + def call(self, inputs): + """Applies batch normalization to the inputs.""" + return self.bn(inputs) + + +class TFPad(keras.layers.Layer): + # Pad inputs in spatial dimensions 1 and 2 + def __init__(self, pad): + """ + Initializes a padding layer for spatial dimensions 1 and 2 with specified padding, supporting both int and tuple + inputs. + + Inputs are + """ + super().__init__() + if isinstance(pad, int): + self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]]) + else: # tuple/list + self.pad = tf.constant([[0, 0], [pad[0], pad[0]], [pad[1], pad[1]], [0, 0]]) + + def call(self, inputs): + """Pads input tensor with zeros using specified padding, suitable for int and tuple pad dimensions.""" + return tf.pad(inputs, self.pad, mode="constant", constant_values=0) + + +class TFConv(keras.layers.Layer): + # Standard convolution + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): + """ + Initializes a standard convolution layer with optional batch normalization and activation; supports only + group=1. + + Inputs are ch_in, ch_out, weights, kernel, stride, padding, groups. + """ + super().__init__() + assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" + # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding) + # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch + conv = keras.layers.Conv2D( + filters=c2, + kernel_size=k, + strides=s, + padding="SAME" if s == 1 else "VALID", + use_bias=not hasattr(w, "bn"), + kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()), + bias_initializer="zeros" if hasattr(w, "bn") else keras.initializers.Constant(w.conv.bias.numpy()), + ) + self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv]) + self.bn = TFBN(w.bn) if hasattr(w, "bn") else tf.identity + self.act = activations(w.act) if act else tf.identity + + def call(self, inputs): + """Applies convolution, batch normalization, and activation function to input tensors.""" + return self.act(self.bn(self.conv(inputs))) + + +class TFDWConv(keras.layers.Layer): + # Depthwise convolution + def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None): + """ + Initializes a depthwise convolution layer with optional batch normalization and activation for TensorFlow + models. + + Input are ch_in, ch_out, weights, kernel, stride, padding, groups. + """ + super().__init__() + assert c2 % c1 == 0, f"TFDWConv() output={c2} must be a multiple of input={c1} channels" + conv = keras.layers.DepthwiseConv2D( + kernel_size=k, + depth_multiplier=c2 // c1, + strides=s, + padding="SAME" if s == 1 else "VALID", + use_bias=not hasattr(w, "bn"), + depthwise_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()), + bias_initializer="zeros" if hasattr(w, "bn") else keras.initializers.Constant(w.conv.bias.numpy()), + ) + self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv]) + self.bn = TFBN(w.bn) if hasattr(w, "bn") else tf.identity + self.act = activations(w.act) if act else tf.identity + + def call(self, inputs): + """Applies convolution, batch normalization, and activation function to input tensors.""" + return self.act(self.bn(self.conv(inputs))) + + +class TFDWConvTranspose2d(keras.layers.Layer): + # Depthwise ConvTranspose2d + def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None): + """ + Initializes depthwise ConvTranspose2D layer with specific channel, kernel, stride, and padding settings. + + Inputs are ch_in, ch_out, weights, kernel, stride, padding, groups. + """ + super().__init__() + assert c1 == c2, f"TFDWConv() output={c2} must be equal to input={c1} channels" + assert k == 4 and p1 == 1, "TFDWConv() only valid for k=4 and p1=1" + weight, bias = w.weight.permute(2, 3, 1, 0).numpy(), w.bias.numpy() + self.c1 = c1 + self.conv = [ + keras.layers.Conv2DTranspose( + filters=1, + kernel_size=k, + strides=s, + padding="VALID", + output_padding=p2, + use_bias=True, + kernel_initializer=keras.initializers.Constant(weight[..., i : i + 1]), + bias_initializer=keras.initializers.Constant(bias[i]), + ) + for i in range(c1) + ] + + def call(self, inputs): + """Processes input through parallel convolutions and concatenates results, trimming border pixels.""" + return tf.concat([m(x) for m, x in zip(self.conv, tf.split(inputs, self.c1, 3))], 3)[:, 1:-1, 1:-1] + + +class TFFocus(keras.layers.Layer): + # Focus wh information into c-space + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): + """ + Initializes TFFocus layer to focus width and height information into channel space with custom convolution + parameters. + + Inputs are ch_in, ch_out, kernel, stride, padding, groups. + """ + super().__init__() + self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv) + + def call(self, inputs): + """ + Performs pixel shuffling and convolution on input tensor, downsampling by 2 and expanding channels by 4. + + Example x(b,w,h,c) -> y(b,w/2,h/2,4c). + """ + inputs = [inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], inputs[:, 1::2, 1::2, :]] + return self.conv(tf.concat(inputs, 3)) + + +class TFBottleneck(keras.layers.Layer): + # Standard bottleneck + def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): + """ + Initializes a standard bottleneck layer for TensorFlow models, expanding and contracting channels with optional + shortcut. + + Arguments are ch_in, ch_out, shortcut, groups, expansion. + """ + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2) + self.add = shortcut and c1 == c2 + + def call(self, inputs): + """Performs forward pass; if shortcut is True & input/output channels match, adds input to the convolution + result. + """ + return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs)) + + +class TFCrossConv(keras.layers.Layer): + # Cross Convolution + def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None): + """Initializes cross convolution layer with optional expansion, grouping, and shortcut addition capabilities.""" + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, (1, k), (1, s), w=w.cv1) + self.cv2 = TFConv(c_, c2, (k, 1), (s, 1), g=g, w=w.cv2) + self.add = shortcut and c1 == c2 + + def call(self, inputs): + """Passes input through two convolutions optionally adding the input if channel dimensions match.""" + return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs)) + + +class TFConv2d(keras.layers.Layer): + # Substitution for PyTorch nn.Conv2D + def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None): + """Initializes a TensorFlow 2D convolution layer, mimicking PyTorch's nn.Conv2D functionality for given filter + sizes and stride. + """ + super().__init__() + assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" + self.conv = keras.layers.Conv2D( + filters=c2, + kernel_size=k, + strides=s, + padding="VALID", + use_bias=bias, + kernel_initializer=keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()), + bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None, + ) + + def call(self, inputs): + """Applies a convolution operation to the inputs and returns the result.""" + return self.conv(inputs) + + +class TFBottleneckCSP(keras.layers.Layer): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): + """ + Initializes CSP bottleneck layer with specified channel sizes, count, shortcut option, groups, and expansion + ratio. + + Inputs are ch_in, ch_out, number, shortcut, groups, expansion. + """ + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2) + self.cv3 = TFConv2d(c_, c_, 1, 1, bias=False, w=w.cv3) + self.cv4 = TFConv(2 * c_, c2, 1, 1, w=w.cv4) + self.bn = TFBN(w.bn) + self.act = lambda x: keras.activations.swish(x) + self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) + + def call(self, inputs): + """Processes input through the model layers, concatenates, normalizes, activates, and reduces the output + dimensions. + """ + y1 = self.cv3(self.m(self.cv1(inputs))) + y2 = self.cv2(inputs) + return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3)))) + + +class TFC3(keras.layers.Layer): + # CSP Bottleneck with 3 convolutions + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): + """ + Initializes CSP Bottleneck with 3 convolutions, supporting optional shortcuts and group convolutions. + + Inputs are ch_in, ch_out, number, shortcut, groups, expansion. + """ + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2) + self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3) + self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) + + def call(self, inputs): + """ + Processes input through a sequence of transformations for object detection (YOLOv5). + + See https://github.com/ultralytics/yolov5. + """ + return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3)) + + +class TFC3x(keras.layers.Layer): + # 3 module with cross-convolutions + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): + """ + Initializes layer with cross-convolutions for enhanced feature extraction in object detection models. + + Inputs are ch_in, ch_out, number, shortcut, groups, expansion. + """ + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2) + self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3) + self.m = keras.Sequential( + [TFCrossConv(c_, c_, k=3, s=1, g=g, e=1.0, shortcut=shortcut, w=w.m[j]) for j in range(n)] + ) + + def call(self, inputs): + """Processes input through cascaded convolutions and merges features, returning the final tensor output.""" + return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3)) + + +class TFSPP(keras.layers.Layer): + # Spatial pyramid pooling layer used in YOLOv3-SPP + def __init__(self, c1, c2, k=(5, 9, 13), w=None): + """Initializes a YOLOv3-SPP layer with specific input/output channels and kernel sizes for pooling.""" + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2) + self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding="SAME") for x in k] + + def call(self, inputs): + """Processes input through two TFConv layers and concatenates with max-pooled outputs at intermediate stage.""" + x = self.cv1(inputs) + return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3)) + + +class TFSPPF(keras.layers.Layer): + # Spatial pyramid pooling-Fast layer + def __init__(self, c1, c2, k=5, w=None): + """Initializes a fast spatial pyramid pooling layer with customizable in/out channels, kernel size, and + weights. + """ + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2) + self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding="SAME") + + def call(self, inputs): + """Executes the model's forward pass, concatenating input features with three max-pooled versions before final + convolution. + """ + x = self.cv1(inputs) + y1 = self.m(x) + y2 = self.m(y1) + return self.cv2(tf.concat([x, y1, y2, self.m(y2)], 3)) + + +class TFDetect(keras.layers.Layer): + # TF YOLOv5 Detect layer + def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None): + """Initializes YOLOv5 detection layer for TensorFlow with configurable classes, anchors, channels, and image + size. + """ + super().__init__() + self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32) + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [tf.zeros(1)] * self.nl # init grid + self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32) + self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]), [self.nl, 1, -1, 1, 2]) + self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] + self.training = False # set to False after building model + self.imgsz = imgsz + for i in range(self.nl): + ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i] + self.grid[i] = self._make_grid(nx, ny) + + def call(self, inputs): + """Performs forward pass through the model layers to predict object bounding boxes and classifications.""" + z = [] # inference output + x = [] + for i in range(self.nl): + x.append(self.m[i](inputs[i])) + # x(bs,20,20,255) to x(bs,3,20,20,85) + ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i] + x[i] = tf.reshape(x[i], [-1, ny * nx, self.na, self.no]) + + if not self.training: # inference + y = x[i] + grid = tf.transpose(self.grid[i], [0, 2, 1, 3]) - 0.5 + anchor_grid = tf.transpose(self.anchor_grid[i], [0, 2, 1, 3]) * 4 + xy = (tf.sigmoid(y[..., 0:2]) * 2 + grid) * self.stride[i] # xy + wh = tf.sigmoid(y[..., 2:4]) ** 2 * anchor_grid + # Normalize xywh to 0-1 to reduce calibration error + xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) + wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) + y = tf.concat([xy, wh, tf.sigmoid(y[..., 4 : 5 + self.nc]), y[..., 5 + self.nc :]], -1) + z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no])) + + return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1),) + + @staticmethod + def _make_grid(nx=20, ny=20): + """Generates a 2D grid of coordinates in (x, y) format with shape [1, 1, ny*nx, 2].""" + # return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny)) + return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32) + + +class TFSegment(TFDetect): + # YOLOv5 Segment head for segmentation models + def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(640, 640), w=None): + """Initializes YOLOv5 Segment head with specified channel depths, anchors, and input size for segmentation + models. + """ + super().__init__(nc, anchors, ch, imgsz, w) + self.nm = nm # number of masks + self.npr = npr # number of protos + self.no = 5 + nc + self.nm # number of outputs per anchor + self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] # output conv + self.proto = TFProto(ch[0], self.npr, self.nm, w=w.proto) # protos + self.detect = TFDetect.call + + def call(self, x): + """Applies detection and proto layers on input, returning detections and optionally protos if training.""" + p = self.proto(x[0]) + # p = TFUpsample(None, scale_factor=4, mode='nearest')(self.proto(x[0])) # (optional) full-size protos + p = tf.transpose(p, [0, 3, 1, 2]) # from shape(1,160,160,32) to shape(1,32,160,160) + x = self.detect(self, x) + return (x, p) if self.training else (x[0], p) + + +class TFProto(keras.layers.Layer): + def __init__(self, c1, c_=256, c2=32, w=None): + """Initializes TFProto layer with convolutional and upsampling layers for feature extraction and + transformation. + """ + super().__init__() + self.cv1 = TFConv(c1, c_, k=3, w=w.cv1) + self.upsample = TFUpsample(None, scale_factor=2, mode="nearest") + self.cv2 = TFConv(c_, c_, k=3, w=w.cv2) + self.cv3 = TFConv(c_, c2, w=w.cv3) + + def call(self, inputs): + """Performs forward pass through the model, applying convolutions and upscaling on input tensor.""" + return self.cv3(self.cv2(self.upsample(self.cv1(inputs)))) + + +class TFUpsample(keras.layers.Layer): + # TF version of torch.nn.Upsample() + def __init__(self, size, scale_factor, mode, w=None): + """ + Initializes a TensorFlow upsampling layer with specified size, scale_factor, and mode, ensuring scale_factor is + even. + + Warning: all arguments needed including 'w' + """ + super().__init__() + assert scale_factor % 2 == 0, "scale_factor must be multiple of 2" + self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * scale_factor, x.shape[2] * scale_factor), mode) + # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode) + # with default arguments: align_corners=False, half_pixel_centers=False + # self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x, + # size=(x.shape[1] * 2, x.shape[2] * 2)) + + def call(self, inputs): + """Applies upsample operation to inputs using nearest neighbor interpolation.""" + return self.upsample(inputs) + + +class TFConcat(keras.layers.Layer): + # TF version of torch.concat() + def __init__(self, dimension=1, w=None): + """Initializes a TensorFlow layer for NCHW to NHWC concatenation, requiring dimension=1.""" + super().__init__() + assert dimension == 1, "convert only NCHW to NHWC concat" + self.d = 3 + + def call(self, inputs): + """Concatenates a list of tensors along the last dimension, used for NCHW to NHWC conversion.""" + return tf.concat(inputs, self.d) + + +def parse_model(d, ch, model, imgsz): + """Parses a model definition dict `d` to create YOLOv5 model layers, including dynamic channel adjustments.""" + LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") + anchors, nc, gd, gw, ch_mul = ( + d["anchors"], + d["nc"], + d["depth_multiple"], + d["width_multiple"], + d.get("channel_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) + if not ch_mul: + ch_mul = 8 + + layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out + for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args + m_str = m + m = eval(m) if isinstance(m, str) else m # eval strings + for j, a in enumerate(args): + try: + args[j] = eval(a) if isinstance(a, str) else a # eval strings + except NameError: + pass + + n = max(round(n * gd), 1) if n > 1 else n # depth gain + if m in [ + nn.Conv2d, + Conv, + DWConv, + DWConvTranspose2d, + Bottleneck, + SPP, + SPPF, + MixConv2d, + Focus, + CrossConv, + BottleneckCSP, + C3, + C3x, + ]: + c1, c2 = ch[f], args[0] + c2 = make_divisible(c2 * gw, ch_mul) if c2 != no else c2 + + args = [c1, c2, *args[1:]] + if m in [BottleneckCSP, C3, C3x]: + args.insert(2, n) + n = 1 + elif m is nn.BatchNorm2d: + args = [ch[f]] + elif m is Concat: + c2 = sum(ch[-1 if x == -1 else x + 1] for x in f) + elif m in [Detect, Segment]: + args.append([ch[x + 1] for x in f]) + if isinstance(args[1], int): # number of anchors + args[1] = [list(range(args[1] * 2))] * len(f) + if m is Segment: + args[3] = make_divisible(args[3] * gw, ch_mul) + args.append(imgsz) + else: + c2 = ch[f] + + tf_m = eval("TF" + m_str.replace("nn.", "")) + m_ = ( + keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) + if n > 1 + else tf_m(*args, w=model.model[i]) + ) # module + + torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module + t = str(m)[8:-2].replace("__main__.", "") # module type + np = sum(x.numel() for x in torch_m_.parameters()) # number params + m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params + LOGGER.info(f"{i:>3}{str(f):>18}{str(n):>3}{np:>10} {t:<40}{str(args):<30}") # print + save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist + layers.append(m_) + ch.append(c2) + return keras.Sequential(layers), sorted(save) + + +class TFModel: + # TF YOLOv5 model + def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, model=None, imgsz=(640, 640)): + """Initializes TF YOLOv5 model with specified configuration, channels, classes, model instance, and input + size. + """ + super().__init__() + if isinstance(cfg, dict): + self.yaml = cfg # model dict + else: # is *.yaml + import yaml # for torch hub + + self.yaml_file = Path(cfg).name + with open(cfg) as f: + self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict + + # Define model + if nc and nc != self.yaml["nc"]: + LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}") + self.yaml["nc"] = nc # override yaml value + self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz) + + def predict( + self, + inputs, + tf_nms=False, + agnostic_nms=False, + topk_per_class=100, + topk_all=100, + iou_thres=0.45, + conf_thres=0.25, + ): + """Runs inference on input data, with an option for TensorFlow NMS.""" + y = [] # outputs + x = inputs + for m in self.model.layers: + if m.f != -1: # if not from previous layer + x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers + + x = m(x) # run + y.append(x if m.i in self.savelist else None) # save output + + # Add TensorFlow NMS + if tf_nms: + boxes = self._xywh2xyxy(x[0][..., :4]) + probs = x[0][:, :, 4:5] + classes = x[0][:, :, 5:] + scores = probs * classes + if agnostic_nms: + nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres) + else: + boxes = tf.expand_dims(boxes, 2) + nms = tf.image.combined_non_max_suppression( + boxes, scores, topk_per_class, topk_all, iou_thres, conf_thres, clip_boxes=False + ) + return (nms,) + return x # output [1,6300,85] = [xywh, conf, class0, class1, ...] + # x = x[0] # [x(1,6300,85), ...] to x(6300,85) + # xywh = x[..., :4] # x(6300,4) boxes + # conf = x[..., 4:5] # x(6300,1) confidences + # cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1)) # x(6300,1) classes + # return tf.concat([conf, cls, xywh], 1) + + @staticmethod + def _xywh2xyxy(xywh): + """Converts bounding box format from [x, y, w, h] to [x1, y1, x2, y2], where xy1=top-left and xy2=bottom- + right. + """ + x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1) + return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1) + + +class AgnosticNMS(keras.layers.Layer): + # TF Agnostic NMS + def call(self, input, topk_all, iou_thres, conf_thres): + """Performs agnostic NMS on input tensors using given thresholds and top-K selection.""" + return tf.map_fn( + lambda x: self._nms(x, topk_all, iou_thres, conf_thres), + input, + fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32), + name="agnostic_nms", + ) + + @staticmethod + def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25): + """Performs agnostic non-maximum suppression (NMS) on detected objects, filtering based on IoU and confidence + thresholds. + """ + boxes, classes, scores = x + class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32) + scores_inp = tf.reduce_max(scores, -1) + selected_inds = tf.image.non_max_suppression( + boxes, scores_inp, max_output_size=topk_all, iou_threshold=iou_thres, score_threshold=conf_thres + ) + selected_boxes = tf.gather(boxes, selected_inds) + padded_boxes = tf.pad( + selected_boxes, + paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]], + mode="CONSTANT", + constant_values=0.0, + ) + selected_scores = tf.gather(scores_inp, selected_inds) + padded_scores = tf.pad( + selected_scores, + paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], + mode="CONSTANT", + constant_values=-1.0, + ) + selected_classes = tf.gather(class_inds, selected_inds) + padded_classes = tf.pad( + selected_classes, + paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], + mode="CONSTANT", + constant_values=-1.0, + ) + valid_detections = tf.shape(selected_inds)[0] + return padded_boxes, padded_scores, padded_classes, valid_detections + + +def activations(act=nn.SiLU): + """Converts PyTorch activations to TensorFlow equivalents, supporting LeakyReLU, Hardswish, and SiLU/Swish.""" + if isinstance(act, nn.LeakyReLU): + return lambda x: keras.activations.relu(x, alpha=0.1) + elif isinstance(act, nn.Hardswish): + return lambda x: x * tf.nn.relu6(x + 3) * 0.166666667 + elif isinstance(act, (nn.SiLU, SiLU)): + return lambda x: keras.activations.swish(x) + else: + raise Exception(f"no matching TensorFlow activation found for PyTorch activation {act}") + + +def representative_dataset_gen(dataset, ncalib=100): + """Generates a representative dataset for calibration by yielding transformed numpy arrays from the input + dataset. + """ + for n, (path, img, im0s, vid_cap, string) in enumerate(dataset): + im = np.transpose(img, [1, 2, 0]) + im = np.expand_dims(im, axis=0).astype(np.float32) + im /= 255 + yield [im] + if n >= ncalib: + break + + +def run( + weights=ROOT / "yolov5s.pt", # weights path + imgsz=(640, 640), # inference size h,w + batch_size=1, # batch size + dynamic=False, # dynamic batch size +): + # PyTorch model + """Exports YOLOv5 model from PyTorch to TensorFlow and Keras formats, performing inference for validation.""" + im = torch.zeros((batch_size, 3, *imgsz)) # BCHW image + model = attempt_load(weights, device=torch.device("cpu"), inplace=True, fuse=False) + _ = model(im) # inference + model.info() + + # TensorFlow model + im = tf.zeros((batch_size, *imgsz, 3)) # BHWC image + tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz) + _ = tf_model.predict(im) # inference + + # Keras model + im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size) + keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im)) + keras_model.summary() + + LOGGER.info("PyTorch, TensorFlow and Keras models successfully verified.\nUse export.py for TF model export.") + + +def parse_opt(): + """Parses and returns command-line options for model inference, including weights path, image size, batch size, and + dynamic batching. + """ + parser = argparse.ArgumentParser() + parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="weights path") + parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w") + parser.add_argument("--batch-size", type=int, default=1, help="batch size") + parser.add_argument("--dynamic", action="store_true", help="dynamic batch size") + opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand + print_args(vars(opt)) + return opt + + +def main(opt): + """Executes the YOLOv5 model run function with parsed command line options.""" + run(**vars(opt)) + + +if __name__ == "__main__": + opt = parse_opt() + main(opt) diff --git a/yolov5/models/yolo.py b/yolov5/models/yolo.py new file mode 100644 index 0000000000000000000000000000000000000000..d89c5da018de1fefe998f42cd45bf23e644f93e9 --- /dev/null +++ b/yolov5/models/yolo.py @@ -0,0 +1,490 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +""" +YOLO-specific modules. + +Usage: + $ python models/yolo.py --cfg yolov5s.yaml +""" + +import argparse +import contextlib +import math +import os +import platform +import sys +from copy import deepcopy +from pathlib import Path + +import torch +import torch.nn as nn + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +if platform.system() != "Windows": + ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import ( + C3, + C3SPP, + C3TR, + SPP, + SPPF, + Bottleneck, + BottleneckCSP, + C3Ghost, + C3x, + Classify, + Concat, + Contract, + Conv, + CrossConv, + DetectMultiBackend, + DWConv, + DWConvTranspose2d, + Expand, + Focus, + GhostBottleneck, + GhostConv, + Proto, +) +from models.experimental import MixConv2d +from utils.autoanchor import check_anchor_order +from utils.general import LOGGER, check_version, check_yaml, colorstr, make_divisible, print_args +from utils.plots import feature_visualization +from utils.torch_utils import ( + fuse_conv_and_bn, + initialize_weights, + model_info, + profile, + scale_img, + select_device, + time_sync, +) + +try: + import thop # for FLOPs computation +except ImportError: + thop = None + + +class Detect(nn.Module): + # YOLOv5 Detect head for detection models + stride = None # strides computed during build + dynamic = False # force grid reconstruction + export = False # export mode + + def __init__(self, nc=80, anchors=(), ch=(), inplace=True): + """Initializes YOLOv5 detection layer with specified classes, anchors, channels, and inplace operations.""" + super().__init__() + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [torch.empty(0) for _ in range(self.nl)] # init grid + self.anchor_grid = [torch.empty(0) for _ in range(self.nl)] # init anchor grid + self.register_buffer("anchors", torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2) + self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv + self.inplace = inplace # use inplace ops (e.g. slice assignment) + + def forward(self, x): + """Processes input through YOLOv5 layers, altering shape for detection: `x(bs, 3, ny, nx, 85)`.""" + z = [] # inference output + for i in range(self.nl): + x[i] = self.m[i](x[i]) # conv + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + + if not self.training: # inference + if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i) + + if isinstance(self, Segment): # (boxes + masks) + xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4) + xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i] # xy + wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i] # wh + y = torch.cat((xy, wh, conf.sigmoid(), mask), 4) + else: # Detect (boxes only) + xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4) + xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy + wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh + y = torch.cat((xy, wh, conf), 4) + z.append(y.view(bs, self.na * nx * ny, self.no)) + + return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x) + + def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, "1.10.0")): + """Generates a mesh grid for anchor boxes with optional compatibility for torch versions < 1.10.""" + d = self.anchors[i].device + t = self.anchors[i].dtype + shape = 1, self.na, ny, nx, 2 # grid shape + y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t) + yv, xv = torch.meshgrid(y, x, indexing="ij") if torch_1_10 else torch.meshgrid(y, x) # torch>=0.7 compatibility + grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5 + anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape) + return grid, anchor_grid + + +class Segment(Detect): + # YOLOv5 Segment head for segmentation models + def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True): + """Initializes YOLOv5 Segment head with options for mask count, protos, and channel adjustments.""" + super().__init__(nc, anchors, ch, inplace) + self.nm = nm # number of masks + self.npr = npr # number of protos + self.no = 5 + nc + self.nm # number of outputs per anchor + self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv + self.proto = Proto(ch[0], self.npr, self.nm) # protos + self.detect = Detect.forward + + def forward(self, x): + """Processes input through the network, returning detections and prototypes; adjusts output based on + training/export mode. + """ + p = self.proto(x[0]) + x = self.detect(self, x) + return (x, p) if self.training else (x[0], p) if self.export else (x[0], p, x[1]) + + +class BaseModel(nn.Module): + """YOLOv5 base model.""" + + def forward(self, x, profile=False, visualize=False): + """Executes a single-scale inference or training pass on the YOLOv5 base model, with options for profiling and + visualization. + """ + return self._forward_once(x, profile, visualize) # single-scale inference, train + + def _forward_once(self, x, profile=False, visualize=False): + """Performs a forward pass on the YOLOv5 model, enabling profiling and feature visualization options.""" + y, dt = [], [] # outputs + for m in self.model: + if m.f != -1: # if not from previous layer + x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers + if profile: + self._profile_one_layer(m, x, dt) + x = m(x) # run + y.append(x if m.i in self.save else None) # save output + if visualize: + feature_visualization(x, m.type, m.i, save_dir=visualize) + return x + + def _profile_one_layer(self, m, x, dt): + """Profiles a single layer's performance by computing GFLOPs, execution time, and parameters.""" + c = m == self.model[-1] # is final layer, copy input as inplace fix + o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1e9 * 2 if thop else 0 # FLOPs + t = time_sync() + for _ in range(10): + m(x.copy() if c else x) + dt.append((time_sync() - t) * 100) + if m == self.model[0]: + LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module") + LOGGER.info(f"{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}") + if c: + LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total") + + def fuse(self): + """Fuses Conv2d() and BatchNorm2d() layers in the model to improve inference speed.""" + LOGGER.info("Fusing layers... ") + for m in self.model.modules(): + if isinstance(m, (Conv, DWConv)) and hasattr(m, "bn"): + m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv + delattr(m, "bn") # remove batchnorm + m.forward = m.forward_fuse # update forward + self.info() + return self + + def info(self, verbose=False, img_size=640): + """Prints model information given verbosity and image size, e.g., `info(verbose=True, img_size=640)`.""" + model_info(self, verbose, img_size) + + def _apply(self, fn): + """Applies transformations like to(), cpu(), cuda(), half() to model tensors excluding parameters or registered + buffers. + """ + self = super()._apply(fn) + m = self.model[-1] # Detect() + if isinstance(m, (Detect, Segment)): + m.stride = fn(m.stride) + m.grid = list(map(fn, m.grid)) + if isinstance(m.anchor_grid, list): + m.anchor_grid = list(map(fn, m.anchor_grid)) + return self + + +class DetectionModel(BaseModel): + # YOLOv5 detection model + def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, anchors=None): + """Initializes YOLOv5 model with configuration file, input channels, number of classes, and custom anchors.""" + super().__init__() + if isinstance(cfg, dict): + self.yaml = cfg # model dict + else: # is *.yaml + import yaml # for torch hub + + self.yaml_file = Path(cfg).name + with open(cfg, encoding="ascii", errors="ignore") as f: + self.yaml = yaml.safe_load(f) # model dict + + # Define model + ch = self.yaml["ch"] = self.yaml.get("ch", ch) # input channels + if nc and nc != self.yaml["nc"]: + LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") + self.yaml["nc"] = nc # override yaml value + if anchors: + LOGGER.info(f"Overriding model.yaml anchors with anchors={anchors}") + self.yaml["anchors"] = round(anchors) # override yaml value + self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist + self.names = [str(i) for i in range(self.yaml["nc"])] # default names + self.inplace = self.yaml.get("inplace", True) + + # Build strides, anchors + m = self.model[-1] # Detect() + if isinstance(m, (Detect, Segment)): + + def _forward(x): + """Passes the input 'x' through the model and returns the processed output.""" + return self.forward(x)[0] if isinstance(m, Segment) else self.forward(x) + + s = 256 # 2x min stride + m.inplace = self.inplace + m.stride = torch.tensor([s / x.shape[-2] for x in _forward(torch.zeros(1, ch, s, s))]) # forward + check_anchor_order(m) + m.anchors /= m.stride.view(-1, 1, 1) + self.stride = m.stride + self._initialize_biases() # only run once + + # Init weights, biases + initialize_weights(self) + self.info() + LOGGER.info("") + + def forward(self, x, augment=False, profile=False, visualize=False): + """Performs single-scale or augmented inference and may include profiling or visualization.""" + if augment: + return self._forward_augment(x) # augmented inference, None + return self._forward_once(x, profile, visualize) # single-scale inference, train + + def _forward_augment(self, x): + """Performs augmented inference across different scales and flips, returning combined detections.""" + img_size = x.shape[-2:] # height, width + s = [1, 0.83, 0.67] # scales + f = [None, 3, None] # flips (2-ud, 3-lr) + y = [] # outputs + for si, fi in zip(s, f): + xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max())) + yi = self._forward_once(xi)[0] # forward + # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save + yi = self._descale_pred(yi, fi, si, img_size) + y.append(yi) + y = self._clip_augmented(y) # clip augmented tails + return torch.cat(y, 1), None # augmented inference, train + + def _descale_pred(self, p, flips, scale, img_size): + """De-scales predictions from augmented inference, adjusting for flips and image size.""" + if self.inplace: + p[..., :4] /= scale # de-scale + if flips == 2: + p[..., 1] = img_size[0] - p[..., 1] # de-flip ud + elif flips == 3: + p[..., 0] = img_size[1] - p[..., 0] # de-flip lr + else: + x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale + if flips == 2: + y = img_size[0] - y # de-flip ud + elif flips == 3: + x = img_size[1] - x # de-flip lr + p = torch.cat((x, y, wh, p[..., 4:]), -1) + return p + + def _clip_augmented(self, y): + """Clips augmented inference tails for YOLOv5 models, affecting first and last tensors based on grid points and + layer counts. + """ + nl = self.model[-1].nl # number of detection layers (P3-P5) + g = sum(4**x for x in range(nl)) # grid points + e = 1 # exclude layer count + i = (y[0].shape[1] // g) * sum(4**x for x in range(e)) # indices + y[0] = y[0][:, :-i] # large + i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices + y[-1] = y[-1][:, i:] # small + return y + + def _initialize_biases(self, cf=None): + """ + Initializes biases for YOLOv5's Detect() module, optionally using class frequencies (cf). + + For details see https://arxiv.org/abs/1708.02002 section 3.3. + """ + # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. + m = self.model[-1] # Detect() module + for mi, s in zip(m.m, m.stride): # from + b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) + b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) + b.data[:, 5 : 5 + m.nc] += ( + math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum()) + ) # cls + mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + + +Model = DetectionModel # retain YOLOv5 'Model' class for backwards compatibility + + +class SegmentationModel(DetectionModel): + # YOLOv5 segmentation model + def __init__(self, cfg="yolov5s-seg.yaml", ch=3, nc=None, anchors=None): + """Initializes a YOLOv5 segmentation model with configurable params: cfg (str) for configuration, ch (int) for channels, nc (int) for num classes, anchors (list).""" + super().__init__(cfg, ch, nc, anchors) + + +class ClassificationModel(BaseModel): + # YOLOv5 classification model + def __init__(self, cfg=None, model=None, nc=1000, cutoff=10): + """Initializes YOLOv5 model with config file `cfg`, input channels `ch`, number of classes `nc`, and `cuttoff` + index. + """ + super().__init__() + self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg) + + def _from_detection_model(self, model, nc=1000, cutoff=10): + """Creates a classification model from a YOLOv5 detection model, slicing at `cutoff` and adding a classification + layer. + """ + if isinstance(model, DetectMultiBackend): + model = model.model # unwrap DetectMultiBackend + model.model = model.model[:cutoff] # backbone + m = model.model[-1] # last layer + ch = m.conv.in_channels if hasattr(m, "conv") else m.cv1.conv.in_channels # ch into module + c = Classify(ch, nc) # Classify() + c.i, c.f, c.type = m.i, m.f, "models.common.Classify" # index, from, type + model.model[-1] = c # replace + self.model = model.model + self.stride = model.stride + self.save = [] + self.nc = nc + + def _from_yaml(self, cfg): + """Creates a YOLOv5 classification model from a specified *.yaml configuration file.""" + self.model = None + + +def parse_model(d, ch): + """Parses a YOLOv5 model from a dict `d`, configuring layers based on input channels `ch` and model architecture.""" + LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") + anchors, nc, gd, gw, act, ch_mul = ( + d["anchors"], + d["nc"], + d["depth_multiple"], + d["width_multiple"], + d.get("activation"), + d.get("channel_multiple"), + ) + if act: + Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU() + LOGGER.info(f"{colorstr('activation:')} {act}") # print + if not ch_mul: + ch_mul = 8 + na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors + no = na * (nc + 5) # number of outputs = anchors * (classes + 5) + + layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out + for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args + m = eval(m) if isinstance(m, str) else m # eval strings + for j, a in enumerate(args): + with contextlib.suppress(NameError): + args[j] = eval(a) if isinstance(a, str) else a # eval strings + + n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain + if m in { + Conv, + GhostConv, + Bottleneck, + GhostBottleneck, + SPP, + SPPF, + DWConv, + MixConv2d, + Focus, + CrossConv, + BottleneckCSP, + C3, + C3TR, + C3SPP, + C3Ghost, + nn.ConvTranspose2d, + DWConvTranspose2d, + C3x, + }: + c1, c2 = ch[f], args[0] + if c2 != no: # if not output + c2 = make_divisible(c2 * gw, ch_mul) + + args = [c1, c2, *args[1:]] + if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}: + args.insert(2, n) # number of repeats + n = 1 + elif m is nn.BatchNorm2d: + args = [ch[f]] + elif m is Concat: + c2 = sum(ch[x] for x in f) + # TODO: channel, gw, gd + elif m in {Detect, Segment}: + args.append([ch[x] for x in f]) + if isinstance(args[1], int): # number of anchors + args[1] = [list(range(args[1] * 2))] * len(f) + if m is Segment: + args[3] = make_divisible(args[3] * gw, ch_mul) + elif m is Contract: + c2 = ch[f] * args[0] ** 2 + elif m is Expand: + c2 = ch[f] // args[0] ** 2 + else: + c2 = ch[f] + + m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module + t = str(m)[8:-2].replace("__main__.", "") # module type + np = sum(x.numel() for x in m_.parameters()) # number params + m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params + LOGGER.info(f"{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}") # print + save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist + layers.append(m_) + if i == 0: + ch = [] + ch.append(c2) + return nn.Sequential(*layers), sorted(save) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--cfg", type=str, default="yolov5s.yaml", help="model.yaml") + parser.add_argument("--batch-size", type=int, default=1, help="total batch size for all GPUs") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--profile", action="store_true", help="profile model speed") + parser.add_argument("--line-profile", action="store_true", help="profile model speed layer by layer") + parser.add_argument("--test", action="store_true", help="test all yolo*.yaml") + opt = parser.parse_args() + opt.cfg = check_yaml(opt.cfg) # check YAML + print_args(vars(opt)) + device = select_device(opt.device) + + # Create model + im = torch.rand(opt.batch_size, 3, 640, 640).to(device) + model = Model(opt.cfg).to(device) + + # Options + if opt.line_profile: # profile layer by layer + model(im, profile=True) + + elif opt.profile: # profile forward-backward + results = profile(input=im, ops=[model], n=3) + + elif opt.test: # test all models + for cfg in Path(ROOT / "models").rglob("yolo*.yaml"): + try: + _ = Model(cfg) + except Exception as e: + print(f"Error in {cfg}: {e}") + + else: # report fused model summary + model.fuse() diff --git a/yolov5/models/yolov5l.yaml b/yolov5/models/yolov5l.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7cac7ead20aa94667e1b151d2417557746cce39c --- /dev/null +++ b/yolov5/models/yolov5l.yaml @@ -0,0 +1,49 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/yolov5m.yaml b/yolov5/models/yolov5m.yaml new file mode 100644 index 0000000000000000000000000000000000000000..820e6070499c54a594a86463e6962f0a9f243cb9 --- /dev/null +++ b/yolov5/models/yolov5m.yaml @@ -0,0 +1,49 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.67 # model depth multiple +width_multiple: 0.75 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/yolov5n.yaml b/yolov5/models/yolov5n.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d3b84ace2b7601c01ba699d9e57019aaa60e1184 --- /dev/null +++ b/yolov5/models/yolov5n.yaml @@ -0,0 +1,49 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.25 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/yolov5s.yaml b/yolov5/models/yolov5s.yaml new file mode 100644 index 0000000000000000000000000000000000000000..090cb67c2bec31183049ec2b671c8d4922696119 --- /dev/null +++ b/yolov5/models/yolov5s.yaml @@ -0,0 +1,49 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/yolov5x.yaml b/yolov5/models/yolov5x.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8c1a6be1b7a84ce77791a5f264e3b4a1517e2211 --- /dev/null +++ b/yolov5/models/yolov5x.yaml @@ -0,0 +1,49 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.33 # model depth multiple +width_multiple: 1.25 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/pyproject.toml b/yolov5/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..8905a28fc702fcb6ad0dec8cc63fde8025eadba0 --- /dev/null +++ b/yolov5/pyproject.toml @@ -0,0 +1,147 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# Overview: +# This pyproject.toml file manages the build, packaging, and distribution of the Ultralytics library. +# It defines essential project metadata, dependencies, and settings used to develop and deploy the library. + +# Key Sections: +# - [build-system]: Specifies the build requirements and backend (e.g., setuptools, wheel). +# - [project]: Includes details like name, version, description, authors, dependencies and more. +# - [project.optional-dependencies]: Provides additional, optional packages for extended features. +# - [tool.*]: Configures settings for various tools (pytest, yapf, etc.) used in the project. + +# Installation: +# The Ultralytics library can be installed using the command: 'pip install ultralytics' +# For development purposes, you can install the package in editable mode with: 'pip install -e .' +# This approach allows for real-time code modifications without the need for re-installation. + +# Documentation: +# For comprehensive documentation and usage instructions, visit: https://docs.ultralytics.com + +[build-system] +requires = ["setuptools>=43.0.0", "wheel"] +build-backend = "setuptools.build_meta" + +# Project settings ----------------------------------------------------------------------------------------------------- +[project] +version = "7.0.0" +name = "YOLOv5" +description = "Ultralytics YOLOv5 for SOTA object detection, instance segmentation and image classification." +readme = "README.md" +requires-python = ">=3.8" +license = { "text" = "AGPL-3.0" } +keywords = ["machine-learning", "deep-learning", "computer-vision", "ML", "DL", "AI", "YOLO", "YOLOv3", "YOLOv5", "YOLOv8", "HUB", "Ultralytics"] +authors = [ + { name = "Glenn Jocher" }, + { name = "Ayush Chaurasia" }, + { name = "Jing Qiu" } +] +maintainers = [ + { name = "Glenn Jocher" }, + { name = "Ayush Chaurasia" }, + { name = "Jing Qiu" } +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "Intended Audience :: Education", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Topic :: Software Development", + "Topic :: Scientific/Engineering", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: Scientific/Engineering :: Image Recognition", + "Operating System :: POSIX :: Linux", + "Operating System :: MacOS", + "Operating System :: Microsoft :: Windows", +] + +# Required dependencies ------------------------------------------------------------------------------------------------ +dependencies = [ + "matplotlib>=3.3.0", + "numpy>=1.22.2", + "opencv-python>=4.6.0", + "pillow>=7.1.2", + "pyyaml>=5.3.1", + "requests>=2.23.0", + "scipy>=1.4.1", + "torch>=1.8.0", + "torchvision>=0.9.0", + "tqdm>=4.64.0", # progress bars + "psutil", # system utilization + "py-cpuinfo", # display CPU info + "thop>=0.1.1", # FLOPs computation + "pandas>=1.1.4", + "seaborn>=0.11.0", # plotting + "ultralytics>=8.1.47" +] + +# Optional dependencies ------------------------------------------------------------------------------------------------ +[project.optional-dependencies] +dev = [ + "ipython", + "check-manifest", + "pre-commit", + "pytest", + "pytest-cov", + "coverage[toml]", + "mkdocs-material", + "mkdocstrings[python]", + "mkdocs-redirects", # for 301 redirects + "mkdocs-ultralytics-plugin>=0.0.34", # for meta descriptions and images, dates and authors +] +export = [ + "onnx>=1.12.0", # ONNX export + "coremltools>=7.0; platform_system != 'Windows'", # CoreML only supported on macOS and Linux + "openvino-dev>=2023.0", # OpenVINO export + "tensorflow<=2.16.1", # TF bug https://github.com/ultralytics/ultralytics/issues/5161 + "tensorflowjs>=3.9.0", # TF.js export, automatically installs tensorflow +] +# tensorflow>=2.4.1,<=2.13.1 # TF exports (-cpu, -aarch64, -macos) +# tflite-support # for TFLite model metadata +# scikit-learn==0.19.2 # CoreML quantization +# nvidia-pyindex # TensorRT export +# nvidia-tensorrt # TensorRT export +logging = [ + "comet", # https://docs.ultralytics.com/integrations/comet/ + "tensorboard>=2.13.0", + "dvclive>=2.12.0", +] +extra = [ + "ipython", # interactive notebook + "albumentations>=1.0.3", # training augmentations + "pycocotools>=2.0.6", # COCO mAP +] + +[project.urls] +"Bug Reports" = "https://github.com/ultralytics/yolov5/issues" +"Funding" = "https://ultralytics.com" +"Source" = "https://github.com/ultralytics/yolov5/" + +# Tools settings ------------------------------------------------------------------------------------------------------- +[tool.pytest] +norecursedirs = [".git", "dist", "build"] +addopts = "--doctest-modules --durations=30 --color=yes" + +[tool.isort] +line_length = 120 +multi_line_output = 0 + +[tool.ruff] +line-length = 120 + +[tool.docformatter] +wrap-summaries = 120 +wrap-descriptions = 120 +in-place = true +pre-summary-newline = true +close-quotes-on-newline = true + +[tool.codespell] +ignore-words-list = "crate,nd,strack,dota,ane,segway,fo,gool,winn,commend" +skip = '*.csv,*venv*,docs/??/,docs/mkdocs_??.yml' diff --git a/yolov5/requirements.txt b/yolov5/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..3b26745962584dae8ad670ec9a196ee948cd4080 --- /dev/null +++ b/yolov5/requirements.txt @@ -0,0 +1,49 @@ +# YOLOv5 requirements +# Usage: pip install -r requirements.txt + +# Base ------------------------------------------------------------------------ +gitpython>=3.1.30 +matplotlib>=3.3 +numpy>=1.23.5 +opencv-python>=4.1.1 +pillow>=10.3.0 +psutil # system resources +PyYAML>=5.3.1 +requests>=2.32.0 +scipy>=1.4.1 +thop>=0.1.1 # FLOPs computation +torch>=1.8.0 # see https://pytorch.org/get-started/locally (recommended) +torchvision>=0.9.0 +tqdm>=4.64.0 +ultralytics>=8.2.34 # https://ultralytics.com +# protobuf<=3.20.1 # https://github.com/ultralytics/yolov5/issues/8012 + +# Logging --------------------------------------------------------------------- +# tensorboard>=2.4.1 +# clearml>=1.2.0 +# comet + +# Plotting -------------------------------------------------------------------- +pandas>=1.1.4 +seaborn>=0.11.0 + +# Export ---------------------------------------------------------------------- +# coremltools>=6.0 # CoreML export +# onnx>=1.10.0 # ONNX export +# onnx-simplifier>=0.4.1 # ONNX simplifier +# nvidia-pyindex # TensorRT export +# nvidia-tensorrt # TensorRT export +# scikit-learn<=1.1.2 # CoreML quantization +# tensorflow>=2.4.0,<=2.13.1 # TF exports (-cpu, -aarch64, -macos) +# tensorflowjs>=3.9.0 # TF.js export +# openvino-dev>=2023.0 # OpenVINO export + +# Deploy ---------------------------------------------------------------------- +setuptools>=65.5.1 # Snyk vulnerability fix +# tritonclient[all]~=2.24.0 + +# Extras ---------------------------------------------------------------------- +# ipython # interactive notebook +# mss # screenshots +# albumentations>=1.0.3 +# pycocotools>=2.0.6 # COCO mAP diff --git a/yolov5/segment/predict.py b/yolov5/segment/predict.py new file mode 100644 index 0000000000000000000000000000000000000000..0bccaaaae9f779319846ffd6e2ebe43641f05a22 --- /dev/null +++ b/yolov5/segment/predict.py @@ -0,0 +1,307 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +""" +Run YOLOv5 segmentation inference on images, videos, directories, streams, etc. + +Usage - sources: + $ python segment/predict.py --weights yolov5s-seg.pt --source 0 # webcam + img.jpg # image + vid.mp4 # video + screen # screenshot + path/ # directory + list.txt # list of images + list.streams # list of streams + 'path/*.jpg' # glob + 'https://youtu.be/LNwODJXcvt4' # YouTube + 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream + +Usage - formats: + $ python segment/predict.py --weights yolov5s-seg.pt # PyTorch + yolov5s-seg.torchscript # TorchScript + yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s-seg_openvino_model # OpenVINO + yolov5s-seg.engine # TensorRT + yolov5s-seg.mlmodel # CoreML (macOS-only) + yolov5s-seg_saved_model # TensorFlow SavedModel + yolov5s-seg.pb # TensorFlow GraphDef + yolov5s-seg.tflite # TensorFlow Lite + yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU + yolov5s-seg_paddle_model # PaddlePaddle +""" + +import argparse +import os +import platform +import sys +from pathlib import Path + +import torch + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from ultralytics.utils.plotting import Annotator, colors, save_one_box + +from models.common import DetectMultiBackend +from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams +from utils.general import ( + LOGGER, + Profile, + check_file, + check_img_size, + check_imshow, + check_requirements, + colorstr, + cv2, + increment_path, + non_max_suppression, + print_args, + scale_boxes, + scale_segments, + strip_optimizer, +) +from utils.segment.general import masks2segments, process_mask, process_mask_native +from utils.torch_utils import select_device, smart_inference_mode + + +@smart_inference_mode() +def run( + weights=ROOT / "yolov5s-seg.pt", # model.pt path(s) + source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam) + data=ROOT / "data/coco128.yaml", # dataset.yaml path + imgsz=(640, 640), # inference size (height, width) + conf_thres=0.25, # confidence threshold + iou_thres=0.45, # NMS IOU threshold + max_det=1000, # maximum detections per image + device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu + view_img=False, # show results + save_txt=False, # save results to *.txt + save_conf=False, # save confidences in --save-txt labels + save_crop=False, # save cropped prediction boxes + nosave=False, # do not save images/videos + classes=None, # filter by class: --class 0, or --class 0 2 3 + agnostic_nms=False, # class-agnostic NMS + augment=False, # augmented inference + visualize=False, # visualize features + update=False, # update all models + project=ROOT / "runs/predict-seg", # save results to project/name + name="exp", # save results to project/name + exist_ok=False, # existing project/name ok, do not increment + line_thickness=3, # bounding box thickness (pixels) + hide_labels=False, # hide labels + hide_conf=False, # hide confidences + half=False, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + vid_stride=1, # video frame-rate stride + retina_masks=False, +): + """Run YOLOv5 segmentation inference on diverse sources including images, videos, directories, and streams.""" + source = str(source) + save_img = not nosave and not source.endswith(".txt") # save inference images + is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) + is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://")) + webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file) + screenshot = source.lower().startswith("screen") + if is_url and is_file: + source = check_file(source) # download + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + device = select_device(device) + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, names, pt = model.stride, model.names, model.pt + imgsz = check_img_size(imgsz, s=stride) # check image size + + # Dataloader + bs = 1 # batch_size + if webcam: + view_img = check_imshow(warn=True) + dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + bs = len(dataset) + elif screenshot: + dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) + else: + dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) + vid_path, vid_writer = [None] * bs, [None] * bs + + # Run inference + model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup + seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device)) + for path, im, im0s, vid_cap, s in dataset: + with dt[0]: + im = torch.from_numpy(im).to(model.device) + im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + if len(im.shape) == 3: + im = im[None] # expand for batch dim + + # Inference + with dt[1]: + visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False + pred, proto = model(im, augment=augment, visualize=visualize)[:2] + + # NMS + with dt[2]: + pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det, nm=32) + + # Second-stage classifier (optional) + # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) + + # Process predictions + for i, det in enumerate(pred): # per image + seen += 1 + if webcam: # batch_size >= 1 + p, im0, frame = path[i], im0s[i].copy(), dataset.count + s += f"{i}: " + else: + p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0) + + p = Path(p) # to Path + save_path = str(save_dir / p.name) # im.jpg + txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt + s += "%gx%g " % im.shape[2:] # print string + imc = im0.copy() if save_crop else im0 # for save_crop + annotator = Annotator(im0, line_width=line_thickness, example=str(names)) + if len(det): + if retina_masks: + # scale bbox first the crop masks + det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size + masks = process_mask_native(proto[i], det[:, 6:], det[:, :4], im0.shape[:2]) # HWC + else: + masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True) # HWC + det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size + + # Segments + if save_txt: + segments = [ + scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=True) + for x in reversed(masks2segments(masks)) + ] + + # Print results + for c in det[:, 5].unique(): + n = (det[:, 5] == c).sum() # detections per class + s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string + + # Mask plotting + annotator.masks( + masks, + colors=[colors(x, True) for x in det[:, 5]], + im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous() + / 255 + if retina_masks + else im[i], + ) + + # Write results + for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])): + if save_txt: # Write to file + seg = segments[j].reshape(-1) # (n,2) to (n*2) + line = (cls, *seg, conf) if save_conf else (cls, *seg) # label format + with open(f"{txt_path}.txt", "a") as f: + f.write(("%g " * len(line)).rstrip() % line + "\n") + + if save_img or save_crop or view_img: # Add bbox to image + c = int(cls) # integer class + label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}") + annotator.box_label(xyxy, label, color=colors(c, True)) + # annotator.draw.polygon(segments[j], outline=colors(c, True), width=3) + if save_crop: + save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True) + + # Stream results + im0 = annotator.result() + if view_img: + if platform.system() == "Linux" and p not in windows: + windows.append(p) + cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) + cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) + cv2.imshow(str(p), im0) + if cv2.waitKey(1) == ord("q"): # 1 millisecond + exit() + + # Save results (image with detections) + if save_img: + if dataset.mode == "image": + cv2.imwrite(save_path, im0) + else: # 'video' or 'stream' + if vid_path[i] != save_path: # new video + vid_path[i] = save_path + if isinstance(vid_writer[i], cv2.VideoWriter): + vid_writer[i].release() # release previous video writer + if vid_cap: # video + fps = vid_cap.get(cv2.CAP_PROP_FPS) + w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + else: # stream + fps, w, h = 30, im0.shape[1], im0.shape[0] + save_path = str(Path(save_path).with_suffix(".mp4")) # force *.mp4 suffix on results videos + vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) + vid_writer[i].write(im0) + + # Print time (inference-only) + LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") + + # Print results + t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image + LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t) + if save_txt or save_img: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else "" + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + if update: + strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) + + +def parse_opt(): + """Parses command-line options for YOLOv5 inference including model paths, data sources, inference settings, and + output preferences. + """ + parser = argparse.ArgumentParser() + parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-seg.pt", help="model path(s)") + parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)") + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path") + parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w") + parser.add_argument("--conf-thres", type=float, default=0.25, help="confidence threshold") + parser.add_argument("--iou-thres", type=float, default=0.45, help="NMS IoU threshold") + parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--view-img", action="store_true", help="show results") + parser.add_argument("--save-txt", action="store_true", help="save results to *.txt") + parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels") + parser.add_argument("--save-crop", action="store_true", help="save cropped prediction boxes") + parser.add_argument("--nosave", action="store_true", help="do not save images/videos") + parser.add_argument("--classes", nargs="+", type=int, help="filter by class: --classes 0, or --classes 0 2 3") + parser.add_argument("--agnostic-nms", action="store_true", help="class-agnostic NMS") + parser.add_argument("--augment", action="store_true", help="augmented inference") + parser.add_argument("--visualize", action="store_true", help="visualize features") + parser.add_argument("--update", action="store_true", help="update all models") + parser.add_argument("--project", default=ROOT / "runs/predict-seg", help="save results to project/name") + parser.add_argument("--name", default="exp", help="save results to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--line-thickness", default=3, type=int, help="bounding box thickness (pixels)") + parser.add_argument("--hide-labels", default=False, action="store_true", help="hide labels") + parser.add_argument("--hide-conf", default=False, action="store_true", help="hide confidences") + parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") + parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") + parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride") + parser.add_argument("--retina-masks", action="store_true", help="whether to plot masks in native resolution") + opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand + print_args(vars(opt)) + return opt + + +def main(opt): + """Executes YOLOv5 model inference with given options, checking for requirements before launching.""" + check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) + run(**vars(opt)) + + +if __name__ == "__main__": + opt = parse_opt() + main(opt) diff --git a/yolov5/segment/train.py b/yolov5/segment/train.py new file mode 100644 index 0000000000000000000000000000000000000000..379fed0b2f142525330c6e685cf925e13e614ed1 --- /dev/null +++ b/yolov5/segment/train.py @@ -0,0 +1,764 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +""" +Train a YOLOv5 segment model on a segment dataset Models and datasets download automatically from the latest YOLOv5 +release. + +Usage - Single-GPU training: + $ python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 # from pretrained (recommended) + $ python segment/train.py --data coco128-seg.yaml --weights '' --cfg yolov5s-seg.yaml --img 640 # from scratch + +Usage - Multi-GPU DDP training: + $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3 + +Models: https://github.com/ultralytics/yolov5/tree/master/models +Datasets: https://github.com/ultralytics/yolov5/tree/master/data +Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data +""" + +import argparse +import math +import os +import random +import subprocess +import sys +import time +from copy import deepcopy +from datetime import datetime +from pathlib import Path + +import numpy as np +import torch +import torch.distributed as dist +import torch.nn as nn +import yaml +from torch.optim import lr_scheduler +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +import segment.val as validate # for end-of-epoch mAP +from models.experimental import attempt_load +from models.yolo import SegmentationModel +from utils.autoanchor import check_anchors +from utils.autobatch import check_train_batch_size +from utils.callbacks import Callbacks +from utils.downloads import attempt_download, is_url +from utils.general import ( + LOGGER, + TQDM_BAR_FORMAT, + check_amp, + check_dataset, + check_file, + check_git_info, + check_git_status, + check_img_size, + check_requirements, + check_suffix, + check_yaml, + colorstr, + get_latest_run, + increment_path, + init_seeds, + intersect_dicts, + labels_to_class_weights, + labels_to_image_weights, + one_cycle, + print_args, + print_mutation, + strip_optimizer, + yaml_save, +) +from utils.loggers import GenericLogger +from utils.plots import plot_evolve, plot_labels +from utils.segment.dataloaders import create_dataloader +from utils.segment.loss import ComputeLoss +from utils.segment.metrics import KEYS, fitness +from utils.segment.plots import plot_images_and_masks, plot_results_with_masks +from utils.torch_utils import ( + EarlyStopping, + ModelEMA, + de_parallel, + select_device, + smart_DDP, + smart_optimizer, + smart_resume, + torch_distributed_zero_first, +) + +LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv("RANK", -1)) +WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) +GIT_INFO = check_git_info() + + +def train(hyp, opt, device, callbacks): + """ + Trains the YOLOv5 model on a dataset, managing hyperparameters, model optimization, logging, and validation. + + `hyp` is path/to/hyp.yaml or hyp dictionary. + """ + ( + save_dir, + epochs, + batch_size, + weights, + single_cls, + evolve, + data, + cfg, + resume, + noval, + nosave, + workers, + freeze, + mask_ratio, + ) = ( + Path(opt.save_dir), + opt.epochs, + opt.batch_size, + opt.weights, + opt.single_cls, + opt.evolve, + opt.data, + opt.cfg, + opt.resume, + opt.noval, + opt.nosave, + opt.workers, + opt.freeze, + opt.mask_ratio, + ) + # callbacks.run('on_pretrain_routine_start') + + # Directories + w = save_dir / "weights" # weights dir + (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir + last, best = w / "last.pt", w / "best.pt" + + # Hyperparameters + if isinstance(hyp, str): + with open(hyp, errors="ignore") as f: + hyp = yaml.safe_load(f) # load hyps dict + LOGGER.info(colorstr("hyperparameters: ") + ", ".join(f"{k}={v}" for k, v in hyp.items())) + opt.hyp = hyp.copy() # for saving hyps to checkpoints + + # Save run settings + if not evolve: + yaml_save(save_dir / "hyp.yaml", hyp) + yaml_save(save_dir / "opt.yaml", vars(opt)) + + # Loggers + data_dict = None + if RANK in {-1, 0}: + logger = GenericLogger(opt=opt, console_logger=LOGGER) + + # Config + plots = not evolve and not opt.noplots # create plots + overlap = not opt.no_overlap + cuda = device.type != "cpu" + init_seeds(opt.seed + 1 + RANK, deterministic=True) + with torch_distributed_zero_first(LOCAL_RANK): + data_dict = data_dict or check_dataset(data) # check if None + train_path, val_path = data_dict["train"], data_dict["val"] + nc = 1 if single_cls else int(data_dict["nc"]) # number of classes + names = {0: "item"} if single_cls and len(data_dict["names"]) != 1 else data_dict["names"] # class names + is_coco = isinstance(val_path, str) and val_path.endswith("coco/val2017.txt") # COCO dataset + + # Model + check_suffix(weights, ".pt") # check weights + pretrained = weights.endswith(".pt") + if pretrained: + with torch_distributed_zero_first(LOCAL_RANK): + weights = attempt_download(weights) # download if not found locally + ckpt = torch.load(weights, map_location="cpu") # load checkpoint to CPU to avoid CUDA memory leak + model = SegmentationModel(cfg or ckpt["model"].yaml, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) + exclude = ["anchor"] if (cfg or hyp.get("anchors")) and not resume else [] # exclude keys + csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32 + csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect + model.load_state_dict(csd, strict=False) # load + LOGGER.info(f"Transferred {len(csd)}/{len(model.state_dict())} items from {weights}") # report + else: + model = SegmentationModel(cfg, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) # create + amp = check_amp(model) # check AMP + + # Freeze + freeze = [f"model.{x}." for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze + for k, v in model.named_parameters(): + v.requires_grad = True # train all layers + # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results) + if any(x in k for x in freeze): + LOGGER.info(f"freezing {k}") + v.requires_grad = False + + # Image size + gs = max(int(model.stride.max()), 32) # grid size (max stride) + imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple + + # Batch size + if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size + batch_size = check_train_batch_size(model, imgsz, amp) + logger.update_params({"batch_size": batch_size}) + # loggers.on_params_update({"batch_size": batch_size}) + + # Optimizer + nbs = 64 # nominal batch size + accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing + hyp["weight_decay"] *= batch_size * accumulate / nbs # scale weight_decay + optimizer = smart_optimizer(model, opt.optimizer, hyp["lr0"], hyp["momentum"], hyp["weight_decay"]) + + # Scheduler + if opt.cos_lr: + lf = one_cycle(1, hyp["lrf"], epochs) # cosine 1->hyp['lrf'] + else: + + def lf(x): + """Linear learning rate scheduler decreasing from 1 to hyp['lrf'] over 'epochs'.""" + return (1 - x / epochs) * (1.0 - hyp["lrf"]) + hyp["lrf"] # linear + + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs) + + # EMA + ema = ModelEMA(model) if RANK in {-1, 0} else None + + # Resume + best_fitness, start_epoch = 0.0, 0 + if pretrained: + if resume: + best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume) + del ckpt, csd + + # DP mode + if cuda and RANK == -1 and torch.cuda.device_count() > 1: + LOGGER.warning( + "WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n" + "See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started." + ) + model = torch.nn.DataParallel(model) + + # SyncBatchNorm + if opt.sync_bn and cuda and RANK != -1: + model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) + LOGGER.info("Using SyncBatchNorm()") + + # Trainloader + train_loader, dataset = create_dataloader( + train_path, + imgsz, + batch_size // WORLD_SIZE, + gs, + single_cls, + hyp=hyp, + augment=True, + cache=None if opt.cache == "val" else opt.cache, + rect=opt.rect, + rank=LOCAL_RANK, + workers=workers, + image_weights=opt.image_weights, + quad=opt.quad, + prefix=colorstr("train: "), + shuffle=True, + mask_downsample_ratio=mask_ratio, + overlap_mask=overlap, + ) + labels = np.concatenate(dataset.labels, 0) + mlc = int(labels[:, 0].max()) # max label class + assert mlc < nc, f"Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}" + + # Process 0 + if RANK in {-1, 0}: + val_loader = create_dataloader( + val_path, + imgsz, + batch_size // WORLD_SIZE * 2, + gs, + single_cls, + hyp=hyp, + cache=None if noval else opt.cache, + rect=True, + rank=-1, + workers=workers * 2, + pad=0.5, + mask_downsample_ratio=mask_ratio, + overlap_mask=overlap, + prefix=colorstr("val: "), + )[0] + + if not resume: + if not opt.noautoanchor: + check_anchors(dataset, model=model, thr=hyp["anchor_t"], imgsz=imgsz) # run AutoAnchor + model.half().float() # pre-reduce anchor precision + + if plots: + plot_labels(labels, names, save_dir) + # callbacks.run('on_pretrain_routine_end', labels, names) + + # DDP mode + if cuda and RANK != -1: + model = smart_DDP(model) + + # Model attributes + nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) + hyp["box"] *= 3 / nl # scale to layers + hyp["cls"] *= nc / 80 * 3 / nl # scale to classes and layers + hyp["obj"] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers + hyp["label_smoothing"] = opt.label_smoothing + model.nc = nc # attach number of classes to model + model.hyp = hyp # attach hyperparameters to model + model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights + model.names = names + + # Start training + t0 = time.time() + nb = len(train_loader) # number of batches + nw = max(round(hyp["warmup_epochs"] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations) + # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training + last_opt_step = -1 + maps = np.zeros(nc) # mAP per class + results = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) + scheduler.last_epoch = start_epoch - 1 # do not move + scaler = torch.cuda.amp.GradScaler(enabled=amp) + stopper, stop = EarlyStopping(patience=opt.patience), False + compute_loss = ComputeLoss(model, overlap=overlap) # init loss class + # callbacks.run('on_train_start') + LOGGER.info( + f'Image sizes {imgsz} train, {imgsz} val\n' + f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n' + f"Logging results to {colorstr('bold', save_dir)}\n" + f'Starting training for {epochs} epochs...' + ) + for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ + # callbacks.run('on_train_epoch_start') + model.train() + + # Update image weights (optional, single-GPU only) + if opt.image_weights: + cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights + iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights + dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx + + # Update mosaic border (optional) + # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) + # dataset.mosaic_border = [b - imgsz, -b] # height, width borders + + mloss = torch.zeros(4, device=device) # mean losses + if RANK != -1: + train_loader.sampler.set_epoch(epoch) + pbar = enumerate(train_loader) + LOGGER.info( + ("\n" + "%11s" * 8) + % ("Epoch", "GPU_mem", "box_loss", "seg_loss", "obj_loss", "cls_loss", "Instances", "Size") + ) + if RANK in {-1, 0}: + pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar + optimizer.zero_grad() + for i, (imgs, targets, paths, _, masks) in pbar: # batch ------------------------------------------------------ + # callbacks.run('on_train_batch_start') + ni = i + nb * epoch # number integrated batches (since train start) + imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0 + + # Warmup + if ni <= nw: + xi = [0, nw] # x interp + # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) + accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round()) + for j, x in enumerate(optimizer.param_groups): + # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 + x["lr"] = np.interp(ni, xi, [hyp["warmup_bias_lr"] if j == 0 else 0.0, x["initial_lr"] * lf(epoch)]) + if "momentum" in x: + x["momentum"] = np.interp(ni, xi, [hyp["warmup_momentum"], hyp["momentum"]]) + + # Multi-scale + if opt.multi_scale: + sz = random.randrange(int(imgsz * 0.5), int(imgsz * 1.5) + gs) // gs * gs # size + sf = sz / max(imgs.shape[2:]) # scale factor + if sf != 1: + ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) + imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False) + + # Forward + with torch.cuda.amp.autocast(amp): + pred = model(imgs) # forward + loss, loss_items = compute_loss(pred, targets.to(device), masks=masks.to(device).float()) + if RANK != -1: + loss *= WORLD_SIZE # gradient averaged between devices in DDP mode + if opt.quad: + loss *= 4.0 + + # Backward + scaler.scale(loss).backward() + + # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html + if ni - last_opt_step >= accumulate: + scaler.unscale_(optimizer) # unscale gradients + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients + scaler.step(optimizer) # optimizer.step + scaler.update() + optimizer.zero_grad() + if ema: + ema.update(model) + last_opt_step = ni + + # Log + if RANK in {-1, 0}: + mloss = (mloss * i + loss_items) / (i + 1) # update mean losses + mem = f"{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G" # (GB) + pbar.set_description( + ("%11s" * 2 + "%11.4g" * 6) + % (f"{epoch}/{epochs - 1}", mem, *mloss, targets.shape[0], imgs.shape[-1]) + ) + # callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths) + # if callbacks.stop_training: + # return + + # Mosaic plots + if plots: + if ni < 3: + plot_images_and_masks(imgs, targets, masks, paths, save_dir / f"train_batch{ni}.jpg") + if ni == 10: + files = sorted(save_dir.glob("train*.jpg")) + logger.log_images(files, "Mosaics", epoch) + # end batch ------------------------------------------------------------------------------------------------ + + # Scheduler + lr = [x["lr"] for x in optimizer.param_groups] # for loggers + scheduler.step() + + if RANK in {-1, 0}: + # mAP + # callbacks.run('on_train_epoch_end', epoch=epoch) + ema.update_attr(model, include=["yaml", "nc", "hyp", "names", "stride", "class_weights"]) + final_epoch = (epoch + 1 == epochs) or stopper.possible_stop + if not noval or final_epoch: # Calculate mAP + results, maps, _ = validate.run( + data_dict, + batch_size=batch_size // WORLD_SIZE * 2, + imgsz=imgsz, + half=amp, + model=ema.ema, + single_cls=single_cls, + dataloader=val_loader, + save_dir=save_dir, + plots=False, + callbacks=callbacks, + compute_loss=compute_loss, + mask_downsample_ratio=mask_ratio, + overlap=overlap, + ) + + # Update best mAP + fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] + stop = stopper(epoch=epoch, fitness=fi) # early stop check + if fi > best_fitness: + best_fitness = fi + log_vals = list(mloss) + list(results) + lr + # callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi) + # Log val metrics and media + metrics_dict = dict(zip(KEYS, log_vals)) + logger.log_metrics(metrics_dict, epoch) + + # Save model + if (not nosave) or (final_epoch and not evolve): # if save + ckpt = { + "epoch": epoch, + "best_fitness": best_fitness, + "model": deepcopy(de_parallel(model)).half(), + "ema": deepcopy(ema.ema).half(), + "updates": ema.updates, + "optimizer": optimizer.state_dict(), + "opt": vars(opt), + "git": GIT_INFO, # {remote, branch, commit} if a git repo + "date": datetime.now().isoformat(), + } + + # Save last, best and delete + torch.save(ckpt, last) + if best_fitness == fi: + torch.save(ckpt, best) + if opt.save_period > 0 and epoch % opt.save_period == 0: + torch.save(ckpt, w / f"epoch{epoch}.pt") + logger.log_model(w / f"epoch{epoch}.pt") + del ckpt + # callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi) + + # EarlyStopping + if RANK != -1: # if DDP training + broadcast_list = [stop if RANK == 0 else None] + dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks + if RANK != 0: + stop = broadcast_list[0] + if stop: + break # must break all DDP ranks + + # end epoch ---------------------------------------------------------------------------------------------------- + # end training ----------------------------------------------------------------------------------------------------- + if RANK in {-1, 0}: + LOGGER.info(f"\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.") + for f in last, best: + if f.exists(): + strip_optimizer(f) # strip optimizers + if f is best: + LOGGER.info(f"\nValidating {f}...") + results, _, _ = validate.run( + data_dict, + batch_size=batch_size // WORLD_SIZE * 2, + imgsz=imgsz, + model=attempt_load(f, device).half(), + iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65 + single_cls=single_cls, + dataloader=val_loader, + save_dir=save_dir, + save_json=is_coco, + verbose=True, + plots=plots, + callbacks=callbacks, + compute_loss=compute_loss, + mask_downsample_ratio=mask_ratio, + overlap=overlap, + ) # val best model with plots + if is_coco: + # callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi) + metrics_dict = dict(zip(KEYS, list(mloss) + list(results) + lr)) + logger.log_metrics(metrics_dict, epoch) + + # callbacks.run('on_train_end', last, best, epoch, results) + # on train end callback using genericLogger + logger.log_metrics(dict(zip(KEYS[4:16], results)), epochs) + if not opt.evolve: + logger.log_model(best, epoch) + if plots: + plot_results_with_masks(file=save_dir / "results.csv") # save results.png + files = ["results.png", "confusion_matrix.png", *(f"{x}_curve.png" for x in ("F1", "PR", "P", "R"))] + files = [(save_dir / f) for f in files if (save_dir / f).exists()] # filter + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}") + logger.log_images(files, "Results", epoch + 1) + logger.log_images(sorted(save_dir.glob("val*.jpg")), "Validation", epoch + 1) + torch.cuda.empty_cache() + return results + + +def parse_opt(known=False): + """ + Parses command line arguments for training configurations, returning parsed arguments. + + Supports both known and unknown args. + """ + parser = argparse.ArgumentParser() + parser.add_argument("--weights", type=str, default=ROOT / "yolov5s-seg.pt", help="initial weights path") + parser.add_argument("--cfg", type=str, default="", help="model.yaml path") + parser.add_argument("--data", type=str, default=ROOT / "data/coco128-seg.yaml", help="dataset.yaml path") + parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path") + parser.add_argument("--epochs", type=int, default=100, help="total training epochs") + parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch") + parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)") + parser.add_argument("--rect", action="store_true", help="rectangular training") + parser.add_argument("--resume", nargs="?", const=True, default=False, help="resume most recent training") + parser.add_argument("--nosave", action="store_true", help="only save final checkpoint") + parser.add_argument("--noval", action="store_true", help="only validate final epoch") + parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor") + parser.add_argument("--noplots", action="store_true", help="save no plot files") + parser.add_argument("--evolve", type=int, nargs="?", const=300, help="evolve hyperparameters for x generations") + parser.add_argument("--bucket", type=str, default="", help="gsutil bucket") + parser.add_argument("--cache", type=str, nargs="?", const="ram", help="image --cache ram/disk") + parser.add_argument("--image-weights", action="store_true", help="use weighted image selection for training") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--multi-scale", action="store_true", help="vary img-size +/- 50%%") + parser.add_argument("--single-cls", action="store_true", help="train multi-class data as single-class") + parser.add_argument("--optimizer", type=str, choices=["SGD", "Adam", "AdamW"], default="SGD", help="optimizer") + parser.add_argument("--sync-bn", action="store_true", help="use SyncBatchNorm, only available in DDP mode") + parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") + parser.add_argument("--project", default=ROOT / "runs/train-seg", help="save to project/name") + parser.add_argument("--name", default="exp", help="save to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--quad", action="store_true", help="quad dataloader") + parser.add_argument("--cos-lr", action="store_true", help="cosine LR scheduler") + parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon") + parser.add_argument("--patience", type=int, default=100, help="EarlyStopping patience (epochs without improvement)") + parser.add_argument("--freeze", nargs="+", type=int, default=[0], help="Freeze layers: backbone=10, first3=0 1 2") + parser.add_argument("--save-period", type=int, default=-1, help="Save checkpoint every x epochs (disabled if < 1)") + parser.add_argument("--seed", type=int, default=0, help="Global training seed") + parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify") + + # Instance Segmentation Args + parser.add_argument("--mask-ratio", type=int, default=4, help="Downsample the truth masks to saving memory") + parser.add_argument("--no-overlap", action="store_true", help="Overlap masks train faster at slightly less mAP") + + return parser.parse_known_args()[0] if known else parser.parse_args() + + +def main(opt, callbacks=Callbacks()): + """Initializes training or evolution of YOLOv5 models based on provided configuration and options.""" + if RANK in {-1, 0}: + print_args(vars(opt)) + check_git_status() + check_requirements(ROOT / "requirements.txt") + + # Resume + if opt.resume and not opt.evolve: # resume from specified or most recent last.pt + last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run()) + opt_yaml = last.parent.parent / "opt.yaml" # train options yaml + opt_data = opt.data # original dataset + if opt_yaml.is_file(): + with open(opt_yaml, errors="ignore") as f: + d = yaml.safe_load(f) + else: + d = torch.load(last, map_location="cpu")["opt"] + opt = argparse.Namespace(**d) # replace + opt.cfg, opt.weights, opt.resume = "", str(last), True # reinstate + if is_url(opt_data): + opt.data = check_file(opt_data) # avoid HUB resume auth timeout + else: + opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = ( + check_file(opt.data), + check_yaml(opt.cfg), + check_yaml(opt.hyp), + str(opt.weights), + str(opt.project), + ) # checks + assert len(opt.cfg) or len(opt.weights), "either --cfg or --weights must be specified" + if opt.evolve: + if opt.project == str(ROOT / "runs/train-seg"): # if default project name, rename to runs/evolve-seg + opt.project = str(ROOT / "runs/evolve-seg") + opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume + if opt.name == "cfg": + opt.name = Path(opt.cfg).stem # use model.yaml as name + opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) + + # DDP mode + device = select_device(opt.device, batch_size=opt.batch_size) + if LOCAL_RANK != -1: + msg = "is not compatible with YOLOv5 Multi-GPU DDP training" + assert not opt.image_weights, f"--image-weights {msg}" + assert not opt.evolve, f"--evolve {msg}" + assert opt.batch_size != -1, f"AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size" + assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE" + assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command" + torch.cuda.set_device(LOCAL_RANK) + device = torch.device("cuda", LOCAL_RANK) + dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo") + + # Train + if not opt.evolve: + train(opt.hyp, opt, device, callbacks) + + # Evolve hyperparameters (optional) + else: + # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) + meta = { + "lr0": (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) + "lrf": (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) + "momentum": (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 + "weight_decay": (1, 0.0, 0.001), # optimizer weight decay + "warmup_epochs": (1, 0.0, 5.0), # warmup epochs (fractions ok) + "warmup_momentum": (1, 0.0, 0.95), # warmup initial momentum + "warmup_bias_lr": (1, 0.0, 0.2), # warmup initial bias lr + "box": (1, 0.02, 0.2), # box loss gain + "cls": (1, 0.2, 4.0), # cls loss gain + "cls_pw": (1, 0.5, 2.0), # cls BCELoss positive_weight + "obj": (1, 0.2, 4.0), # obj loss gain (scale with pixels) + "obj_pw": (1, 0.5, 2.0), # obj BCELoss positive_weight + "iou_t": (0, 0.1, 0.7), # IoU training threshold + "anchor_t": (1, 2.0, 8.0), # anchor-multiple threshold + "anchors": (2, 2.0, 10.0), # anchors per output grid (0 to ignore) + "fl_gamma": (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) + "hsv_h": (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) + "hsv_s": (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) + "hsv_v": (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) + "degrees": (1, 0.0, 45.0), # image rotation (+/- deg) + "translate": (1, 0.0, 0.9), # image translation (+/- fraction) + "scale": (1, 0.0, 0.9), # image scale (+/- gain) + "shear": (1, 0.0, 10.0), # image shear (+/- deg) + "perspective": (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 + "flipud": (1, 0.0, 1.0), # image flip up-down (probability) + "fliplr": (0, 0.0, 1.0), # image flip left-right (probability) + "mosaic": (1, 0.0, 1.0), # image mixup (probability) + "mixup": (1, 0.0, 1.0), # image mixup (probability) + "copy_paste": (1, 0.0, 1.0), + } # segment copy-paste (probability) + + with open(opt.hyp, errors="ignore") as f: + hyp = yaml.safe_load(f) # load hyps dict + if "anchors" not in hyp: # anchors commented in hyp.yaml + hyp["anchors"] = 3 + if opt.noautoanchor: + del hyp["anchors"], meta["anchors"] + opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch + # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices + evolve_yaml, evolve_csv = save_dir / "hyp_evolve.yaml", save_dir / "evolve.csv" + if opt.bucket: + # download evolve.csv if exists + subprocess.run( + [ + "gsutil", + "cp", + f"gs://{opt.bucket}/evolve.csv", + str(evolve_csv), + ] + ) + + for _ in range(opt.evolve): # generations to evolve + if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate + # Select parent(s) + parent = "single" # parent selection method: 'single' or 'weighted' + x = np.loadtxt(evolve_csv, ndmin=2, delimiter=",", skiprows=1) + n = min(5, len(x)) # number of previous results to consider + x = x[np.argsort(-fitness(x))][:n] # top n mutations + w = fitness(x) - fitness(x).min() + 1e-6 # weights (sum > 0) + if parent == "single" or len(x) == 1: + # x = x[random.randint(0, n - 1)] # random selection + x = x[random.choices(range(n), weights=w)[0]] # weighted selection + elif parent == "weighted": + x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination + + # Mutate + mp, s = 0.8, 0.2 # mutation probability, sigma + npr = np.random + npr.seed(int(time.time())) + g = np.array([meta[k][0] for k in hyp.keys()]) # gains 0-1 + ng = len(meta) + v = np.ones(ng) + while all(v == 1): # mutate until a change occurs (prevent duplicates) + v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) + for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) + hyp[k] = float(x[i + 12] * v[i]) # mutate + + # Constrain to limits + for k, v in meta.items(): + hyp[k] = max(hyp[k], v[1]) # lower limit + hyp[k] = min(hyp[k], v[2]) # upper limit + hyp[k] = round(hyp[k], 5) # significant digits + + # Train mutation + results = train(hyp.copy(), opt, device, callbacks) + callbacks = Callbacks() + # Write mutation results + print_mutation(KEYS[4:16], results, hyp.copy(), save_dir, opt.bucket) + + # Plot results + plot_evolve(evolve_csv) + LOGGER.info( + f'Hyperparameter evolution finished {opt.evolve} generations\n' + f"Results saved to {colorstr('bold', save_dir)}\n" + f'Usage example: $ python train.py --hyp {evolve_yaml}' + ) + + +def run(**kwargs): + """ + Executes YOLOv5 training with given parameters, altering options programmatically; returns updated options. + + Example: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt') + """ + opt = parse_opt(True) + for k, v in kwargs.items(): + setattr(opt, k, v) + main(opt) + return opt + + +if __name__ == "__main__": + opt = parse_opt() + main(opt) diff --git a/yolov5/segment/tutorial.ipynb b/yolov5/segment/tutorial.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0e6091869b7c93f902528ee3ef849bd0577e06e2 --- /dev/null +++ b/yolov5/segment/tutorial.ipynb @@ -0,0 +1,595 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "t6MPjfT5NrKQ" + }, + "source": [ + "
\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "
\n", + " \"Run\n", + " \"Open\n", + " \"Open\n", + "
\n", + "\n", + "This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
See GitHub for community support or contact us for professional support.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7mGmQbAO5pQb" + }, + "source": [ + "# Setup\n", + "\n", + "Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wbvMlHd_QwMG", + "outputId": "171b23f0-71b9-4cbf-b666-6fa2ecef70c8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\n" + ] + } + ], + "source": [ + "!git clone https://github.com/ultralytics/yolov5 # clone\n", + "%cd yolov5\n", + "%pip install -qr requirements.txt comet_ml # install\n", + "\n", + "import torch\n", + "import utils\n", + "display = utils.notebook_init() # checks" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4JnkELT0cIJg" + }, + "source": [ + "# 1. Predict\n", + "\n", + "`segment/predict.py` runs YOLOv5 instance segmentation inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/predict`. Example inference sources are:\n", + "\n", + "```shell\n", + "python segment/predict.py --source 0 # webcam\n", + " img.jpg # image \n", + " vid.mp4 # video\n", + " screen # screenshot\n", + " path/ # directory\n", + " 'path/*.jpg' # glob\n", + " 'https://youtu.be/LNwODJXcvt4' # YouTube\n", + " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zR9ZbuQCH7FX", + "outputId": "3f67f1c7-f15e-4fa5-d251-967c3b77eaad" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1msegment/predict: \u001b[0mweights=['yolov5s-seg.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/predict-seg, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1, retina_masks=False\n", + "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt to yolov5s-seg.pt...\n", + "100% 14.9M/14.9M [00:01<00:00, 12.0MB/s]\n", + "\n", + "Fusing layers... \n", + "YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n", + "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 18.2ms\n", + "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, 13.4ms\n", + "Speed: 0.5ms pre-process, 15.8ms inference, 18.5ms NMS per image at shape (1, 3, 640, 640)\n", + "Results saved to \u001b[1mruns/predict-seg/exp\u001b[0m\n" + ] + } + ], + "source": [ + "!python segment/predict.py --weights yolov5s-seg.pt --img 640 --conf 0.25 --source data/images\n", + "#display.Image(filename='runs/predict-seg/exp/zidane.jpg', width=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hkAzDWJ7cWTr" + }, + "source": [ + "        \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0eq1SMWl6Sfn" + }, + "source": [ + "# 2. Validate\n", + "Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WQPtK1QYVaD_", + "outputId": "9d751d8c-bee8-4339-cf30-9854ca530449" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels-segments.zip ...\n", + "Downloading http://images.cocodataset.org/zips/val2017.zip ...\n", + "######################################################################## 100.0%\n", + "######################################################################## 100.0%\n" + ] + } + ], + "source": [ + "# Download COCO val\n", + "!bash data/scripts/get_coco.sh --val --segments # download (780M - 5000 images)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X58w8JLpMnjH", + "outputId": "a140d67a-02da-479e-9ddb-7d54bf9e407a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1msegment/val: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s-seg.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val-seg, name=exp, exist_ok=False, half=True, dnn=False\n", + "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "Fusing layers... \n", + "YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:03<00:00, 1361.31it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n", + " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 157/157 [01:54<00:00, 1.37it/s]\n", + " all 5000 36335 0.673 0.517 0.566 0.373 0.672 0.49 0.532 0.319\n", + "Speed: 0.6ms pre-process, 4.4ms inference, 2.9ms NMS per image at shape (32, 3, 640, 640)\n", + "Results saved to \u001b[1mruns/val-seg/exp\u001b[0m\n" + ] + } + ], + "source": [ + "# Validate YOLOv5s-seg on COCO val\n", + "!python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 --half" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZY2VXXXu74w5" + }, + "source": [ + "# 3. Train\n", + "\n", + "

\n", + "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n", + "

\n", + "\n", + "Train a YOLOv5s-seg model on the [COCO128](https://www.kaggle.com/ultralytics/coco128) dataset with `--data coco128-seg.yaml`, starting from pretrained `--weights yolov5s-seg.pt`, or from randomly initialized `--weights '' --cfg yolov5s-seg.yaml`.\n", + "\n", + "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n", + "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n", + "- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n", + "- **Training Results** are saved to `runs/train-seg/` with incrementing run directories, i.e. `runs/train-seg/exp2`, `runs/train-seg/exp3` etc.\n", + "

\n", + "\n", + "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n", + "\n", + "## Train on Custom Data with Roboflow 🌟 NEW\n", + "\n", + "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n", + "\n", + "- Custom Training Example: [https://blog.roboflow.com/train-yolov5-instance-segmentation-custom-dataset/](https://blog.roboflow.com/train-yolov5-instance-segmentation-custom-dataset/?ref=ultralytics)\n", + "- Custom Training Notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1JTz7kpmHsg-5qwVz2d2IH3AaenI1tv0N?usp=sharing)\n", + "
\n", + "\n", + "

Label images lightning fast (including with model-assisted labeling)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "i3oKtE4g-aNn" + }, + "outputs": [], + "source": [ + "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n", + "logger = 'Comet' #@param ['Comet', 'ClearML', 'TensorBoard']\n", + "\n", + "if logger == 'Comet':\n", + " %pip install -q comet_ml\n", + " import comet_ml; comet_ml.init()\n", + "elif logger == 'ClearML':\n", + " %pip install -q clearml\n", + " import clearml; clearml.browser_login()\n", + "elif logger == 'TensorBoard':\n", + " %load_ext tensorboard\n", + " %tensorboard --logdir runs/train" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "1NcFxRcFdJ_O", + "outputId": "3a3e0cf7-e79c-47a5-c8e7-2d26eeeab988" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1msegment/train: \u001b[0mweights=yolov5s-seg.pt, cfg=, data=coco128-seg.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train-seg, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, mask_ratio=4, no_overlap=False\n", + "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", + "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n", + "\n", + "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n", + "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-seg', view at http://localhost:6006/\n", + "\n", + "Dataset not found ⚠️, missing paths ['/content/datasets/coco128-seg/images/train2017']\n", + "Downloading https://ultralytics.com/assets/coco128-seg.zip to coco128-seg.zip...\n", + "100% 6.79M/6.79M [00:01<00:00, 6.73MB/s]\n", + "Dataset download success ✅ (1.9s), saved to \u001b[1m/content/datasets\u001b[0m\n", + "\n", + " from n params module arguments \n", + " 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n", + " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", + " 2 -1 1 18816 models.common.C3 [64, 64, 1] \n", + " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", + " 4 -1 2 115712 models.common.C3 [128, 128, 2] \n", + " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n", + " 6 -1 3 625152 models.common.C3 [256, 256, 3] \n", + " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n", + " 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n", + " 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n", + " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 12 [-1, 6] 1 0 models.common.Concat [1] \n", + " 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n", + " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", + " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 16 [-1, 4] 1 0 models.common.Concat [1] \n", + " 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n", + " 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n", + " 19 [-1, 14] 1 0 models.common.Concat [1] \n", + " 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n", + " 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n", + " 22 [-1, 10] 1 0 models.common.Concat [1] \n", + " 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n", + " 24 [17, 20, 23] 1 615133 models.yolo.Segment [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], 32, 128, [128, 256, 512]]\n", + "Model summary: 225 layers, 7621277 parameters, 7621277 gradients, 26.6 GFLOPs\n", + "\n", + "Transferred 367/367 items from yolov5s-seg.pt\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", + "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 60 weight(decay=0.0), 63 weight(decay=0.0005), 63 bias\n", + "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128-seg/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1389.59it/s]\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128-seg/labels/train2017.cache\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 238.86it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128-seg/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00 # 2. paste API key\n", + "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n", + "```\n", + "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n", + "\n", + "\n", + "\"Comet" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Lay2WsTjNJzP" + }, + "source": [ + "## ClearML Logging and Automation 🌟 NEW\n", + "\n", + "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n", + "\n", + "- `pip install clearml`\n", + "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n", + "\n", + "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n", + "\n", + "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n", + "\n", + "\n", + "\"ClearML" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-WPvRbS5Swl6" + }, + "source": [ + "## Local Logging\n", + "\n", + "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n", + "\n", + "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n", + "\n", + "\"Local\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zelyeqbyt3GD" + }, + "source": [ + "# Environments\n", + "\n", + "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n", + "\n", + "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", + "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n", + "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n", + "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \"Docker\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Qu7Iesl0p54" + }, + "source": [ + "# Status\n", + "\n", + "![YOLOv5 CI](https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg)\n", + "\n", + "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IEijrePND_2I" + }, + "source": [ + "# Appendix\n", + "\n", + "Additional content below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GMusP4OAxFu6" + }, + "outputs": [], + "source": [ + "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n", + "import torch\n", + "\n", + "model = torch.hub.load('ultralytics/yolov5', 'yolov5s-seg', force_reload=True, trust_repo=True) # or yolov5n - yolov5x6 or custom\n", + "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n", + "results = model(im) # inference\n", + "results.print() # or .show(), .save(), .crop(), .pandas(), etc." + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "YOLOv5 Segmentation Tutorial", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/yolov5/segment/val.py b/yolov5/segment/val.py new file mode 100644 index 0000000000000000000000000000000000000000..ab8a66a90c3481b982a8571c57b9de9a582a2e18 --- /dev/null +++ b/yolov5/segment/val.py @@ -0,0 +1,522 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +""" +Validate a trained YOLOv5 segment model on a segment dataset. + +Usage: + $ bash data/scripts/get_coco.sh --val --segments # download COCO-segments val split (1G, 5000 images) + $ python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate COCO-segments + +Usage - formats: + $ python segment/val.py --weights yolov5s-seg.pt # PyTorch + yolov5s-seg.torchscript # TorchScript + yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s-seg_openvino_label # OpenVINO + yolov5s-seg.engine # TensorRT + yolov5s-seg.mlmodel # CoreML (macOS-only) + yolov5s-seg_saved_model # TensorFlow SavedModel + yolov5s-seg.pb # TensorFlow GraphDef + yolov5s-seg.tflite # TensorFlow Lite + yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU + yolov5s-seg_paddle_model # PaddlePaddle +""" + +import argparse +import json +import os +import subprocess +import sys +from multiprocessing.pool import ThreadPool +from pathlib import Path + +import numpy as np +import torch +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +import torch.nn.functional as F + +from models.common import DetectMultiBackend +from models.yolo import SegmentationModel +from utils.callbacks import Callbacks +from utils.general import ( + LOGGER, + NUM_THREADS, + TQDM_BAR_FORMAT, + Profile, + check_dataset, + check_img_size, + check_requirements, + check_yaml, + coco80_to_coco91_class, + colorstr, + increment_path, + non_max_suppression, + print_args, + scale_boxes, + xywh2xyxy, + xyxy2xywh, +) +from utils.metrics import ConfusionMatrix, box_iou +from utils.plots import output_to_target, plot_val_study +from utils.segment.dataloaders import create_dataloader +from utils.segment.general import mask_iou, process_mask, process_mask_native, scale_image +from utils.segment.metrics import Metrics, ap_per_class_box_and_mask +from utils.segment.plots import plot_images_and_masks +from utils.torch_utils import de_parallel, select_device, smart_inference_mode + + +def save_one_txt(predn, save_conf, shape, file): + """Saves detection results in txt format; includes class, xywh (normalized), optionally confidence if `save_conf` is + True. + """ + gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh + for *xyxy, conf, cls in predn.tolist(): + xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format + with open(file, "a") as f: + f.write(("%g " * len(line)).rstrip() % line + "\n") + + +def save_one_json(predn, jdict, path, class_map, pred_masks): + """ + Saves a JSON file with detection results including bounding boxes, category IDs, scores, and segmentation masks. + + Example JSON result: {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}. + """ + from pycocotools.mask import encode + + def single_encode(x): + """Encodes binary mask arrays into RLE (Run-Length Encoding) format for JSON serialization.""" + rle = encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0] + rle["counts"] = rle["counts"].decode("utf-8") + return rle + + image_id = int(path.stem) if path.stem.isnumeric() else path.stem + box = xyxy2xywh(predn[:, :4]) # xywh + box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner + pred_masks = np.transpose(pred_masks, (2, 0, 1)) + with ThreadPool(NUM_THREADS) as pool: + rles = pool.map(single_encode, pred_masks) + for i, (p, b) in enumerate(zip(predn.tolist(), box.tolist())): + jdict.append( + { + "image_id": image_id, + "category_id": class_map[int(p[5])], + "bbox": [round(x, 3) for x in b], + "score": round(p[4], 5), + "segmentation": rles[i], + } + ) + + +def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, overlap=False, masks=False): + """ + Return correct prediction matrix + Arguments: + detections (array[N, 6]), x1, y1, x2, y2, conf, class + labels (array[M, 5]), class, x1, y1, x2, y2 + Returns: + correct (array[N, 10]), for 10 IoU levels + """ + if masks: + if overlap: + nl = len(labels) + index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1 + gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640) + gt_masks = torch.where(gt_masks == index, 1.0, 0.0) + if gt_masks.shape[1:] != pred_masks.shape[1:]: + gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode="bilinear", align_corners=False)[0] + gt_masks = gt_masks.gt_(0.5) + iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1)) + else: # boxes + iou = box_iou(labels[:, 1:], detections[:, :4]) + + correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool) + correct_class = labels[:, 0:1] == detections[:, 5] + for i in range(len(iouv)): + x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match + if x[0].shape[0]: + matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou] + if x[0].shape[0] > 1: + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 1], return_index=True)[1]] + # matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 0], return_index=True)[1]] + correct[matches[:, 1].astype(int), i] = True + return torch.tensor(correct, dtype=torch.bool, device=iouv.device) + + +@smart_inference_mode() +def run( + data, + weights=None, # model.pt path(s) + batch_size=32, # batch size + imgsz=640, # inference size (pixels) + conf_thres=0.001, # confidence threshold + iou_thres=0.6, # NMS IoU threshold + max_det=300, # maximum detections per image + task="val", # train, val, test, speed or study + device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu + workers=8, # max dataloader workers (per RANK in DDP mode) + single_cls=False, # treat as single-class dataset + augment=False, # augmented inference + verbose=False, # verbose output + save_txt=False, # save results to *.txt + save_hybrid=False, # save label+prediction hybrid results to *.txt + save_conf=False, # save confidences in --save-txt labels + save_json=False, # save a COCO-JSON results file + project=ROOT / "runs/val-seg", # save to project/name + name="exp", # save to project/name + exist_ok=False, # existing project/name ok, do not increment + half=True, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + model=None, + dataloader=None, + save_dir=Path(""), + plots=True, + overlap=False, + mask_downsample_ratio=1, + compute_loss=None, + callbacks=Callbacks(), +): + """Validates a YOLOv5 segmentation model on specified dataset, producing metrics, plots, and optional JSON + output. + """ + if save_json: + check_requirements("pycocotools>=2.0.6") + process = process_mask_native # more accurate + else: + process = process_mask # faster + + # Initialize/load model and set device + training = model is not None + if training: # called by train.py + device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model + half &= device.type != "cpu" # half precision only supported on CUDA + model.half() if half else model.float() + nm = de_parallel(model).model[-1].nm # number of masks + else: # called directly + device = select_device(device, batch_size=batch_size) + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine + imgsz = check_img_size(imgsz, s=stride) # check image size + half = model.fp16 # FP16 supported on limited backends with CUDA + nm = de_parallel(model).model.model[-1].nm if isinstance(model, SegmentationModel) else 32 # number of masks + if engine: + batch_size = model.batch_size + else: + device = model.device + if not (pt or jit): + batch_size = 1 # export.py models default to batch-size 1 + LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models") + + # Data + data = check_dataset(data) # check + + # Configure + model.eval() + cuda = device.type != "cpu" + is_coco = isinstance(data.get("val"), str) and data["val"].endswith(f"coco{os.sep}val2017.txt") # COCO dataset + nc = 1 if single_cls else int(data["nc"]) # number of classes + iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95 + niou = iouv.numel() + + # Dataloader + if not training: + if pt and not single_cls: # check --weights are trained on --data + ncm = model.model.nc + assert ncm == nc, ( + f"{weights} ({ncm} classes) trained on different --data than what you passed ({nc} " + f"classes). Pass correct combination of --weights and --data that are trained together." + ) + model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup + pad, rect = (0.0, False) if task == "speed" else (0.5, pt) # square inference for benchmarks + task = task if task in ("train", "val", "test") else "val" # path to train/val/test images + dataloader = create_dataloader( + data[task], + imgsz, + batch_size, + stride, + single_cls, + pad=pad, + rect=rect, + workers=workers, + prefix=colorstr(f"{task}: "), + overlap_mask=overlap, + mask_downsample_ratio=mask_downsample_ratio, + )[0] + + seen = 0 + confusion_matrix = ConfusionMatrix(nc=nc) + names = model.names if hasattr(model, "names") else model.module.names # get class names + if isinstance(names, (list, tuple)): # old format + names = dict(enumerate(names)) + class_map = coco80_to_coco91_class() if is_coco else list(range(1000)) + s = ("%22s" + "%11s" * 10) % ( + "Class", + "Images", + "Instances", + "Box(P", + "R", + "mAP50", + "mAP50-95)", + "Mask(P", + "R", + "mAP50", + "mAP50-95)", + ) + dt = Profile(device=device), Profile(device=device), Profile(device=device) + metrics = Metrics() + loss = torch.zeros(4, device=device) + jdict, stats = [], [] + # callbacks.run('on_val_start') + pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar + for batch_i, (im, targets, paths, shapes, masks) in enumerate(pbar): + # callbacks.run('on_val_batch_start') + with dt[0]: + if cuda: + im = im.to(device, non_blocking=True) + targets = targets.to(device) + masks = masks.to(device) + masks = masks.float() + im = im.half() if half else im.float() # uint8 to fp16/32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + nb, _, height, width = im.shape # batch size, channels, height, width + + # Inference + with dt[1]: + preds, protos, train_out = model(im) if compute_loss else (*model(im, augment=augment)[:2], None) + + # Loss + if compute_loss: + loss += compute_loss((train_out, protos), targets, masks)[1] # box, obj, cls + + # NMS + targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels + lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling + with dt[2]: + preds = non_max_suppression( + preds, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls, max_det=max_det, nm=nm + ) + + # Metrics + plot_masks = [] # masks for plotting + for si, (pred, proto) in enumerate(zip(preds, protos)): + labels = targets[targets[:, 0] == si, 1:] + nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions + path, shape = Path(paths[si]), shapes[si][0] + correct_masks = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init + correct_bboxes = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init + seen += 1 + + if npr == 0: + if nl: + stats.append((correct_masks, correct_bboxes, *torch.zeros((2, 0), device=device), labels[:, 0])) + if plots: + confusion_matrix.process_batch(detections=None, labels=labels[:, 0]) + continue + + # Masks + midx = [si] if overlap else targets[:, 0] == si + gt_masks = masks[midx] + pred_masks = process(proto, pred[:, 6:], pred[:, :4], shape=im[si].shape[1:]) + + # Predictions + if single_cls: + pred[:, 5] = 0 + predn = pred.clone() + scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred + + # Evaluate + if nl: + tbox = xywh2xyxy(labels[:, 1:5]) # target boxes + scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels + labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels + correct_bboxes = process_batch(predn, labelsn, iouv) + correct_masks = process_batch(predn, labelsn, iouv, pred_masks, gt_masks, overlap=overlap, masks=True) + if plots: + confusion_matrix.process_batch(predn, labelsn) + stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # (conf, pcls, tcls) + + pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8) + if plots and batch_i < 3: + plot_masks.append(pred_masks[:15]) # filter top 15 to plot + + # Save/log + if save_txt: + save_one_txt(predn, save_conf, shape, file=save_dir / "labels" / f"{path.stem}.txt") + if save_json: + pred_masks = scale_image( + im[si].shape[1:], pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1] + ) + save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary + # callbacks.run('on_val_image_end', pred, predn, path, names, im[si]) + + # Plot images + if plots and batch_i < 3: + if len(plot_masks): + plot_masks = torch.cat(plot_masks, dim=0) + plot_images_and_masks(im, targets, masks, paths, save_dir / f"val_batch{batch_i}_labels.jpg", names) + plot_images_and_masks( + im, + output_to_target(preds, max_det=15), + plot_masks, + paths, + save_dir / f"val_batch{batch_i}_pred.jpg", + names, + ) # pred + + # callbacks.run('on_val_batch_end') + + # Compute metrics + stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy + if len(stats) and stats[0].any(): + results = ap_per_class_box_and_mask(*stats, plot=plots, save_dir=save_dir, names=names) + metrics.update(results) + nt = np.bincount(stats[4].astype(int), minlength=nc) # number of targets per class + + # Print results + pf = "%22s" + "%11i" * 2 + "%11.3g" * 8 # print format + LOGGER.info(pf % ("all", seen, nt.sum(), *metrics.mean_results())) + if nt.sum() == 0: + LOGGER.warning(f"WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels") + + # Print results per class + if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): + for i, c in enumerate(metrics.ap_class_index): + LOGGER.info(pf % (names[c], seen, nt[c], *metrics.class_result(i))) + + # Print speeds + t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image + if not training: + shape = (batch_size, 3, imgsz, imgsz) + LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}" % t) + + # Plots + if plots: + confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) + # callbacks.run('on_val_end') + + mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask = metrics.mean_results() + + # Save JSON + if save_json and len(jdict): + w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else "" # weights + anno_json = str(Path("../datasets/coco/annotations/instances_val2017.json")) # annotations + pred_json = str(save_dir / f"{w}_predictions.json") # predictions + LOGGER.info(f"\nEvaluating pycocotools mAP... saving {pred_json}...") + with open(pred_json, "w") as f: + json.dump(jdict, f) + + try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb + from pycocotools.coco import COCO + from pycocotools.cocoeval import COCOeval + + anno = COCO(anno_json) # init annotations api + pred = anno.loadRes(pred_json) # init predictions api + results = [] + for eval in COCOeval(anno, pred, "bbox"), COCOeval(anno, pred, "segm"): + if is_coco: + eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # img ID to evaluate + eval.evaluate() + eval.accumulate() + eval.summarize() + results.extend(eval.stats[:2]) # update results (mAP@0.5:0.95, mAP@0.5) + map_bbox, map50_bbox, map_mask, map50_mask = results + except Exception as e: + LOGGER.info(f"pycocotools unable to run: {e}") + + # Return results + model.float() # for training + if not training: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else "" + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + final_metric = mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask + return (*final_metric, *(loss.cpu() / len(dataloader)).tolist()), metrics.get_maps(nc), t + + +def parse_opt(): + """Parses command line arguments for configuring YOLOv5 options like dataset path, weights, batch size, and + inference settings. + """ + parser = argparse.ArgumentParser() + parser.add_argument("--data", type=str, default=ROOT / "data/coco128-seg.yaml", help="dataset.yaml path") + parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-seg.pt", help="model path(s)") + parser.add_argument("--batch-size", type=int, default=32, help="batch size") + parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)") + parser.add_argument("--conf-thres", type=float, default=0.001, help="confidence threshold") + parser.add_argument("--iou-thres", type=float, default=0.6, help="NMS IoU threshold") + parser.add_argument("--max-det", type=int, default=300, help="maximum detections per image") + parser.add_argument("--task", default="val", help="train, val, test, speed or study") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") + parser.add_argument("--single-cls", action="store_true", help="treat as single-class dataset") + parser.add_argument("--augment", action="store_true", help="augmented inference") + parser.add_argument("--verbose", action="store_true", help="report mAP by class") + parser.add_argument("--save-txt", action="store_true", help="save results to *.txt") + parser.add_argument("--save-hybrid", action="store_true", help="save label+prediction hybrid results to *.txt") + parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels") + parser.add_argument("--save-json", action="store_true", help="save a COCO-JSON results file") + parser.add_argument("--project", default=ROOT / "runs/val-seg", help="save results to project/name") + parser.add_argument("--name", default="exp", help="save to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") + parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") + opt = parser.parse_args() + opt.data = check_yaml(opt.data) # check YAML + # opt.save_json |= opt.data.endswith('coco.yaml') + opt.save_txt |= opt.save_hybrid + print_args(vars(opt)) + return opt + + +def main(opt): + """Executes YOLOv5 tasks including training, validation, testing, speed, and study with configurable options.""" + check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) + + if opt.task in ("train", "val", "test"): # run normally + if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466 + LOGGER.warning(f"WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results") + if opt.save_hybrid: + LOGGER.warning("WARNING ⚠️ --save-hybrid returns high mAP from hybrid labels, not from predictions alone") + run(**vars(opt)) + + else: + weights = opt.weights if isinstance(opt.weights, list) else [opt.weights] + opt.half = torch.cuda.is_available() and opt.device != "cpu" # FP16 for fastest results + if opt.task == "speed": # speed benchmarks + # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt... + opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False + for opt.weights in weights: + run(**vars(opt), plots=False) + + elif opt.task == "study": # speed vs mAP benchmarks + # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt... + for opt.weights in weights: + f = f"study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt" # filename to save to + x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis + for opt.imgsz in x: # img-size + LOGGER.info(f"\nRunning {f} --imgsz {opt.imgsz}...") + r, _, t = run(**vars(opt), plots=False) + y.append(r + t) # results and times + np.savetxt(f, y, fmt="%10.4g") # save + subprocess.run(["zip", "-r", "study.zip", "study_*.txt"]) + plot_val_study(x=x) # plot + else: + raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")') + + +if __name__ == "__main__": + opt = parse_opt() + main(opt) diff --git a/yolov5/train.py b/yolov5/train.py new file mode 100644 index 0000000000000000000000000000000000000000..472c1d395981bfa1a40fc04713e8fa69f82a4ce7 --- /dev/null +++ b/yolov5/train.py @@ -0,0 +1,852 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +""" +Train a YOLOv5 model on a custom dataset. Models and datasets download automatically from the latest YOLOv5 release. + +Usage - Single-GPU training: + $ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended) + $ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch + +Usage - Multi-GPU DDP training: + $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights yolov5s.pt --img 640 --device 0,1,2,3 + +Models: https://github.com/ultralytics/yolov5/tree/master/models +Datasets: https://github.com/ultralytics/yolov5/tree/master/data +Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data +""" + +import argparse +import math +import os +import random +import subprocess +import sys +import time +from copy import deepcopy +from datetime import datetime, timedelta +from pathlib import Path + +try: + import comet_ml # must be imported before torch (if installed) +except ImportError: + comet_ml = None + +import numpy as np +import torch +import torch.distributed as dist +import torch.nn as nn +import yaml +from torch.optim import lr_scheduler +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +import val as validate # for end-of-epoch mAP +from models.experimental import attempt_load +from models.yolo import Model +from utils.autoanchor import check_anchors +from utils.autobatch import check_train_batch_size +from utils.callbacks import Callbacks +from utils.dataloaders import create_dataloader +from utils.downloads import attempt_download, is_url +from utils.general import ( + LOGGER, + TQDM_BAR_FORMAT, + check_amp, + check_dataset, + check_file, + check_git_info, + check_git_status, + check_img_size, + check_requirements, + check_suffix, + check_yaml, + colorstr, + get_latest_run, + increment_path, + init_seeds, + intersect_dicts, + labels_to_class_weights, + labels_to_image_weights, + methods, + one_cycle, + print_args, + print_mutation, + strip_optimizer, + yaml_save, +) +from utils.loggers import LOGGERS, Loggers +from utils.loggers.comet.comet_utils import check_comet_resume +from utils.loss import ComputeLoss +from utils.metrics import fitness +from utils.plots import plot_evolve +from utils.torch_utils import ( + EarlyStopping, + ModelEMA, + de_parallel, + select_device, + smart_DDP, + smart_optimizer, + smart_resume, + torch_distributed_zero_first, +) + +LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv("RANK", -1)) +WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) +GIT_INFO = check_git_info() + + +def train(hyp, opt, device, callbacks): + """ + Trains YOLOv5 model with given hyperparameters, options, and device, managing datasets, model architecture, loss + computation, and optimizer steps. + + `hyp` argument is path/to/hyp.yaml or hyp dictionary. + """ + save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = ( + Path(opt.save_dir), + opt.epochs, + opt.batch_size, + opt.weights, + opt.single_cls, + opt.evolve, + opt.data, + opt.cfg, + opt.resume, + opt.noval, + opt.nosave, + opt.workers, + opt.freeze, + ) + callbacks.run("on_pretrain_routine_start") + + # Directories + w = save_dir / "weights" # weights dir + (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir + last, best = w / "last.pt", w / "best.pt" + + # Hyperparameters + if isinstance(hyp, str): + with open(hyp, errors="ignore") as f: + hyp = yaml.safe_load(f) # load hyps dict + LOGGER.info(colorstr("hyperparameters: ") + ", ".join(f"{k}={v}" for k, v in hyp.items())) + opt.hyp = hyp.copy() # for saving hyps to checkpoints + + # Save run settings + if not evolve: + yaml_save(save_dir / "hyp.yaml", hyp) + yaml_save(save_dir / "opt.yaml", vars(opt)) + + # Loggers + data_dict = None + if RANK in {-1, 0}: + include_loggers = list(LOGGERS) + if getattr(opt, "ndjson_console", False): + include_loggers.append("ndjson_console") + if getattr(opt, "ndjson_file", False): + include_loggers.append("ndjson_file") + + loggers = Loggers( + save_dir=save_dir, + weights=weights, + opt=opt, + hyp=hyp, + logger=LOGGER, + include=tuple(include_loggers), + ) + + # Register actions + for k in methods(loggers): + callbacks.register_action(k, callback=getattr(loggers, k)) + + # Process custom dataset artifact link + data_dict = loggers.remote_dataset + if resume: # If resuming runs from remote artifact + weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size + + # Config + plots = not evolve and not opt.noplots # create plots + cuda = device.type != "cpu" + init_seeds(opt.seed + 1 + RANK, deterministic=True) + with torch_distributed_zero_first(LOCAL_RANK): + data_dict = data_dict or check_dataset(data) # check if None + train_path, val_path = data_dict["train"], data_dict["val"] + nc = 1 if single_cls else int(data_dict["nc"]) # number of classes + names = {0: "item"} if single_cls and len(data_dict["names"]) != 1 else data_dict["names"] # class names + is_coco = isinstance(val_path, str) and val_path.endswith("coco/val2017.txt") # COCO dataset + + # Model + check_suffix(weights, ".pt") # check weights + pretrained = weights.endswith(".pt") + if pretrained: + with torch_distributed_zero_first(LOCAL_RANK): + weights = attempt_download(weights) # download if not found locally + ckpt = torch.load(weights, map_location="cpu") # load checkpoint to CPU to avoid CUDA memory leak + model = Model(cfg or ckpt["model"].yaml, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) # create + exclude = ["anchor"] if (cfg or hyp.get("anchors")) and not resume else [] # exclude keys + csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32 + csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect + model.load_state_dict(csd, strict=False) # load + LOGGER.info(f"Transferred {len(csd)}/{len(model.state_dict())} items from {weights}") # report + else: + model = Model(cfg, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) # create + amp = check_amp(model) # check AMP + + # Freeze + freeze = [f"model.{x}." for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze + for k, v in model.named_parameters(): + v.requires_grad = True # train all layers + # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results) + if any(x in k for x in freeze): + LOGGER.info(f"freezing {k}") + v.requires_grad = False + + # Image size + gs = max(int(model.stride.max()), 32) # grid size (max stride) + imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple + + # Batch size + if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size + batch_size = check_train_batch_size(model, imgsz, amp) + loggers.on_params_update({"batch_size": batch_size}) + + # Optimizer + nbs = 64 # nominal batch size + accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing + hyp["weight_decay"] *= batch_size * accumulate / nbs # scale weight_decay + optimizer = smart_optimizer(model, opt.optimizer, hyp["lr0"], hyp["momentum"], hyp["weight_decay"]) + + # Scheduler + if opt.cos_lr: + lf = one_cycle(1, hyp["lrf"], epochs) # cosine 1->hyp['lrf'] + else: + + def lf(x): + """Linear learning rate scheduler function with decay calculated by epoch proportion.""" + return (1 - x / epochs) * (1.0 - hyp["lrf"]) + hyp["lrf"] # linear + + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs) + + # EMA + ema = ModelEMA(model) if RANK in {-1, 0} else None + + # Resume + best_fitness, start_epoch = 0.0, 0 + if pretrained: + if resume: + best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume) + del ckpt, csd + + # DP mode + if cuda and RANK == -1 and torch.cuda.device_count() > 1: + LOGGER.warning( + "WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n" + "See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started." + ) + model = torch.nn.DataParallel(model) + + # SyncBatchNorm + if opt.sync_bn and cuda and RANK != -1: + model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) + LOGGER.info("Using SyncBatchNorm()") + + # Trainloader + train_loader, dataset = create_dataloader( + train_path, + imgsz, + batch_size // WORLD_SIZE, + gs, + single_cls, + hyp=hyp, + augment=True, + cache=None if opt.cache == "val" else opt.cache, + rect=opt.rect, + rank=LOCAL_RANK, + workers=workers, + image_weights=opt.image_weights, + quad=opt.quad, + prefix=colorstr("train: "), + shuffle=True, + seed=opt.seed, + ) + labels = np.concatenate(dataset.labels, 0) + mlc = int(labels[:, 0].max()) # max label class + assert mlc < nc, f"Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}" + + # Process 0 + if RANK in {-1, 0}: + val_loader = create_dataloader( + val_path, + imgsz, + batch_size // WORLD_SIZE * 2, + gs, + single_cls, + hyp=hyp, + cache=None if noval else opt.cache, + rect=True, + rank=-1, + workers=workers * 2, + pad=0.5, + prefix=colorstr("val: "), + )[0] + + if not resume: + if not opt.noautoanchor: + check_anchors(dataset, model=model, thr=hyp["anchor_t"], imgsz=imgsz) # run AutoAnchor + model.half().float() # pre-reduce anchor precision + + callbacks.run("on_pretrain_routine_end", labels, names) + + # DDP mode + if cuda and RANK != -1: + model = smart_DDP(model) + + # Model attributes + nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) + hyp["box"] *= 3 / nl # scale to layers + hyp["cls"] *= nc / 80 * 3 / nl # scale to classes and layers + hyp["obj"] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers + hyp["label_smoothing"] = opt.label_smoothing + model.nc = nc # attach number of classes to model + model.hyp = hyp # attach hyperparameters to model + model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights + model.names = names + + # Start training + t0 = time.time() + nb = len(train_loader) # number of batches + nw = max(round(hyp["warmup_epochs"] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations) + # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training + last_opt_step = -1 + maps = np.zeros(nc) # mAP per class + results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) + scheduler.last_epoch = start_epoch - 1 # do not move + scaler = torch.cuda.amp.GradScaler(enabled=amp) + stopper, stop = EarlyStopping(patience=opt.patience), False + compute_loss = ComputeLoss(model) # init loss class + callbacks.run("on_train_start") + LOGGER.info( + f'Image sizes {imgsz} train, {imgsz} val\n' + f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n' + f"Logging results to {colorstr('bold', save_dir)}\n" + f'Starting training for {epochs} epochs...' + ) + for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ + callbacks.run("on_train_epoch_start") + model.train() + + # Update image weights (optional, single-GPU only) + if opt.image_weights: + cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights + iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights + dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx + + # Update mosaic border (optional) + # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) + # dataset.mosaic_border = [b - imgsz, -b] # height, width borders + + mloss = torch.zeros(3, device=device) # mean losses + if RANK != -1: + train_loader.sampler.set_epoch(epoch) + pbar = enumerate(train_loader) + LOGGER.info(("\n" + "%11s" * 7) % ("Epoch", "GPU_mem", "box_loss", "obj_loss", "cls_loss", "Instances", "Size")) + if RANK in {-1, 0}: + pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar + optimizer.zero_grad() + for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- + callbacks.run("on_train_batch_start") + ni = i + nb * epoch # number integrated batches (since train start) + imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0 + + # Warmup + if ni <= nw: + xi = [0, nw] # x interp + # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) + accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round()) + for j, x in enumerate(optimizer.param_groups): + # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 + x["lr"] = np.interp(ni, xi, [hyp["warmup_bias_lr"] if j == 0 else 0.0, x["initial_lr"] * lf(epoch)]) + if "momentum" in x: + x["momentum"] = np.interp(ni, xi, [hyp["warmup_momentum"], hyp["momentum"]]) + + # Multi-scale + if opt.multi_scale: + sz = random.randrange(int(imgsz * 0.5), int(imgsz * 1.5) + gs) // gs * gs # size + sf = sz / max(imgs.shape[2:]) # scale factor + if sf != 1: + ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) + imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False) + + # Forward + with torch.cuda.amp.autocast(amp): + pred = model(imgs) # forward + loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size + if RANK != -1: + loss *= WORLD_SIZE # gradient averaged between devices in DDP mode + if opt.quad: + loss *= 4.0 + + # Backward + scaler.scale(loss).backward() + + # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html + if ni - last_opt_step >= accumulate: + scaler.unscale_(optimizer) # unscale gradients + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients + scaler.step(optimizer) # optimizer.step + scaler.update() + optimizer.zero_grad() + if ema: + ema.update(model) + last_opt_step = ni + + # Log + if RANK in {-1, 0}: + mloss = (mloss * i + loss_items) / (i + 1) # update mean losses + mem = f"{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G" # (GB) + pbar.set_description( + ("%11s" * 2 + "%11.4g" * 5) + % (f"{epoch}/{epochs - 1}", mem, *mloss, targets.shape[0], imgs.shape[-1]) + ) + callbacks.run("on_train_batch_end", model, ni, imgs, targets, paths, list(mloss)) + if callbacks.stop_training: + return + # end batch ------------------------------------------------------------------------------------------------ + + # Scheduler + lr = [x["lr"] for x in optimizer.param_groups] # for loggers + scheduler.step() + + if RANK in {-1, 0}: + # mAP + callbacks.run("on_train_epoch_end", epoch=epoch) + ema.update_attr(model, include=["yaml", "nc", "hyp", "names", "stride", "class_weights"]) + final_epoch = (epoch + 1 == epochs) or stopper.possible_stop + if not noval or final_epoch: # Calculate mAP + results, maps, _ = validate.run( + data_dict, + batch_size=batch_size // WORLD_SIZE * 2, + imgsz=imgsz, + half=amp, + model=ema.ema, + single_cls=single_cls, + dataloader=val_loader, + save_dir=save_dir, + plots=False, + callbacks=callbacks, + compute_loss=compute_loss, + ) + + # Update best mAP + fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] + stop = stopper(epoch=epoch, fitness=fi) # early stop check + if fi > best_fitness: + best_fitness = fi + log_vals = list(mloss) + list(results) + lr + callbacks.run("on_fit_epoch_end", log_vals, epoch, best_fitness, fi) + + # Save model + if (not nosave) or (final_epoch and not evolve): # if save + ckpt = { + "epoch": epoch, + "best_fitness": best_fitness, + "model": deepcopy(de_parallel(model)).half(), + "ema": deepcopy(ema.ema).half(), + "updates": ema.updates, + "optimizer": optimizer.state_dict(), + "opt": vars(opt), + "git": GIT_INFO, # {remote, branch, commit} if a git repo + "date": datetime.now().isoformat(), + } + + # Save last, best and delete + torch.save(ckpt, last) + if best_fitness == fi: + torch.save(ckpt, best) + if opt.save_period > 0 and epoch % opt.save_period == 0: + torch.save(ckpt, w / f"epoch{epoch}.pt") + del ckpt + callbacks.run("on_model_save", last, epoch, final_epoch, best_fitness, fi) + + # EarlyStopping + if RANK != -1: # if DDP training + broadcast_list = [stop if RANK == 0 else None] + dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks + if RANK != 0: + stop = broadcast_list[0] + if stop: + break # must break all DDP ranks + + # end epoch ---------------------------------------------------------------------------------------------------- + # end training ----------------------------------------------------------------------------------------------------- + if RANK in {-1, 0}: + LOGGER.info(f"\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.") + for f in last, best: + if f.exists(): + strip_optimizer(f) # strip optimizers + if f is best: + LOGGER.info(f"\nValidating {f}...") + results, _, _ = validate.run( + data_dict, + batch_size=batch_size // WORLD_SIZE * 2, + imgsz=imgsz, + model=attempt_load(f, device).half(), + iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65 + single_cls=single_cls, + dataloader=val_loader, + save_dir=save_dir, + save_json=is_coco, + verbose=True, + plots=plots, + callbacks=callbacks, + compute_loss=compute_loss, + ) # val best model with plots + if is_coco: + callbacks.run("on_fit_epoch_end", list(mloss) + list(results) + lr, epoch, best_fitness, fi) + + callbacks.run("on_train_end", last, best, epoch, results) + + torch.cuda.empty_cache() + return results + + +def parse_opt(known=False): + """Parses command-line arguments for YOLOv5 training, validation, and testing.""" + parser = argparse.ArgumentParser() + parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="initial weights path") + parser.add_argument("--cfg", type=str, default="", help="model.yaml path") + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") + parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path") + parser.add_argument("--epochs", type=int, default=100, help="total training epochs") + parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch") + parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)") + parser.add_argument("--rect", action="store_true", help="rectangular training") + parser.add_argument("--resume", nargs="?", const=True, default=False, help="resume most recent training") + parser.add_argument("--nosave", action="store_true", help="only save final checkpoint") + parser.add_argument("--noval", action="store_true", help="only validate final epoch") + parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor") + parser.add_argument("--noplots", action="store_true", help="save no plot files") + parser.add_argument("--evolve", type=int, nargs="?", const=300, help="evolve hyperparameters for x generations") + parser.add_argument( + "--evolve_population", type=str, default=ROOT / "data/hyps", help="location for loading population" + ) + parser.add_argument("--resume_evolve", type=str, default=None, help="resume evolve from last generation") + parser.add_argument("--bucket", type=str, default="", help="gsutil bucket") + parser.add_argument("--cache", type=str, nargs="?", const="ram", help="image --cache ram/disk") + parser.add_argument("--image-weights", action="store_true", help="use weighted image selection for training") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--multi-scale", action="store_true", help="vary img-size +/- 50%%") + parser.add_argument("--single-cls", action="store_true", help="train multi-class data as single-class") + parser.add_argument("--optimizer", type=str, choices=["SGD", "Adam", "AdamW"], default="SGD", help="optimizer") + parser.add_argument("--sync-bn", action="store_true", help="use SyncBatchNorm, only available in DDP mode") + parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") + parser.add_argument("--project", default=ROOT / "runs/train", help="save to project/name") + parser.add_argument("--name", default="exp", help="save to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--quad", action="store_true", help="quad dataloader") + parser.add_argument("--cos-lr", action="store_true", help="cosine LR scheduler") + parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon") + parser.add_argument("--patience", type=int, default=100, help="EarlyStopping patience (epochs without improvement)") + parser.add_argument("--freeze", nargs="+", type=int, default=[0], help="Freeze layers: backbone=10, first3=0 1 2") + parser.add_argument("--save-period", type=int, default=-1, help="Save checkpoint every x epochs (disabled if < 1)") + parser.add_argument("--seed", type=int, default=0, help="Global training seed") + parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify") + + # Logger arguments + parser.add_argument("--entity", default=None, help="Entity") + parser.add_argument("--upload_dataset", nargs="?", const=True, default=False, help='Upload data, "val" option') + parser.add_argument("--bbox_interval", type=int, default=-1, help="Set bounding-box image logging interval") + parser.add_argument("--artifact_alias", type=str, default="latest", help="Version of dataset artifact to use") + + # NDJSON logging + parser.add_argument("--ndjson-console", action="store_true", help="Log ndjson to console") + parser.add_argument("--ndjson-file", action="store_true", help="Log ndjson to file") + + return parser.parse_known_args()[0] if known else parser.parse_args() + + +def main(opt, callbacks=Callbacks()): + """Runs training or hyperparameter evolution with specified options and optional callbacks.""" + if RANK in {-1, 0}: + print_args(vars(opt)) + check_git_status() + check_requirements(ROOT / "requirements.txt") + + # Resume (from specified or most recent last.pt) + if opt.resume and not check_comet_resume(opt) and not opt.evolve: + last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run()) + opt_yaml = last.parent.parent / "opt.yaml" # train options yaml + opt_data = opt.data # original dataset + if opt_yaml.is_file(): + with open(opt_yaml, errors="ignore") as f: + d = yaml.safe_load(f) + else: + d = torch.load(last, map_location="cpu")["opt"] + opt = argparse.Namespace(**d) # replace + opt.cfg, opt.weights, opt.resume = "", str(last), True # reinstate + if is_url(opt_data): + opt.data = check_file(opt_data) # avoid HUB resume auth timeout + else: + opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = ( + check_file(opt.data), + check_yaml(opt.cfg), + check_yaml(opt.hyp), + str(opt.weights), + str(opt.project), + ) # checks + assert len(opt.cfg) or len(opt.weights), "either --cfg or --weights must be specified" + if opt.evolve: + if opt.project == str(ROOT / "runs/train"): # if default project name, rename to runs/evolve + opt.project = str(ROOT / "runs/evolve") + opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume + if opt.name == "cfg": + opt.name = Path(opt.cfg).stem # use model.yaml as name + opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) + + # DDP mode + device = select_device(opt.device, batch_size=opt.batch_size) + if LOCAL_RANK != -1: + msg = "is not compatible with YOLOv5 Multi-GPU DDP training" + assert not opt.image_weights, f"--image-weights {msg}" + assert not opt.evolve, f"--evolve {msg}" + assert opt.batch_size != -1, f"AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size" + assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE" + assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command" + torch.cuda.set_device(LOCAL_RANK) + device = torch.device("cuda", LOCAL_RANK) + dist.init_process_group( + backend="nccl" if dist.is_nccl_available() else "gloo", timeout=timedelta(seconds=10800) + ) + + # Train + if not opt.evolve: + train(opt.hyp, opt, device, callbacks) + + # Evolve hyperparameters (optional) + else: + # Hyperparameter evolution metadata (including this hyperparameter True-False, lower_limit, upper_limit) + meta = { + "lr0": (False, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) + "lrf": (False, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) + "momentum": (False, 0.6, 0.98), # SGD momentum/Adam beta1 + "weight_decay": (False, 0.0, 0.001), # optimizer weight decay + "warmup_epochs": (False, 0.0, 5.0), # warmup epochs (fractions ok) + "warmup_momentum": (False, 0.0, 0.95), # warmup initial momentum + "warmup_bias_lr": (False, 0.0, 0.2), # warmup initial bias lr + "box": (False, 0.02, 0.2), # box loss gain + "cls": (False, 0.2, 4.0), # cls loss gain + "cls_pw": (False, 0.5, 2.0), # cls BCELoss positive_weight + "obj": (False, 0.2, 4.0), # obj loss gain (scale with pixels) + "obj_pw": (False, 0.5, 2.0), # obj BCELoss positive_weight + "iou_t": (False, 0.1, 0.7), # IoU training threshold + "anchor_t": (False, 2.0, 8.0), # anchor-multiple threshold + "anchors": (False, 2.0, 10.0), # anchors per output grid (0 to ignore) + "fl_gamma": (False, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) + "hsv_h": (True, 0.0, 0.1), # image HSV-Hue augmentation (fraction) + "hsv_s": (True, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) + "hsv_v": (True, 0.0, 0.9), # image HSV-Value augmentation (fraction) + "degrees": (True, 0.0, 45.0), # image rotation (+/- deg) + "translate": (True, 0.0, 0.9), # image translation (+/- fraction) + "scale": (True, 0.0, 0.9), # image scale (+/- gain) + "shear": (True, 0.0, 10.0), # image shear (+/- deg) + "perspective": (True, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 + "flipud": (True, 0.0, 1.0), # image flip up-down (probability) + "fliplr": (True, 0.0, 1.0), # image flip left-right (probability) + "mosaic": (True, 0.0, 1.0), # image mixup (probability) + "mixup": (True, 0.0, 1.0), # image mixup (probability) + "copy_paste": (True, 0.0, 1.0), + } # segment copy-paste (probability) + + # GA configs + pop_size = 50 + mutation_rate_min = 0.01 + mutation_rate_max = 0.5 + crossover_rate_min = 0.5 + crossover_rate_max = 1 + min_elite_size = 2 + max_elite_size = 5 + tournament_size_min = 2 + tournament_size_max = 10 + + with open(opt.hyp, errors="ignore") as f: + hyp = yaml.safe_load(f) # load hyps dict + if "anchors" not in hyp: # anchors commented in hyp.yaml + hyp["anchors"] = 3 + if opt.noautoanchor: + del hyp["anchors"], meta["anchors"] + opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch + # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices + evolve_yaml, evolve_csv = save_dir / "hyp_evolve.yaml", save_dir / "evolve.csv" + if opt.bucket: + # download evolve.csv if exists + subprocess.run( + [ + "gsutil", + "cp", + f"gs://{opt.bucket}/evolve.csv", + str(evolve_csv), + ] + ) + + # Delete the items in meta dictionary whose first value is False + del_ = [item for item, value_ in meta.items() if value_[0] is False] + hyp_GA = hyp.copy() # Make a copy of hyp dictionary + for item in del_: + del meta[item] # Remove the item from meta dictionary + del hyp_GA[item] # Remove the item from hyp_GA dictionary + + # Set lower_limit and upper_limit arrays to hold the search space boundaries + lower_limit = np.array([meta[k][1] for k in hyp_GA.keys()]) + upper_limit = np.array([meta[k][2] for k in hyp_GA.keys()]) + + # Create gene_ranges list to hold the range of values for each gene in the population + gene_ranges = [(lower_limit[i], upper_limit[i]) for i in range(len(upper_limit))] + + # Initialize the population with initial_values or random values + initial_values = [] + + # If resuming evolution from a previous checkpoint + if opt.resume_evolve is not None: + assert os.path.isfile(ROOT / opt.resume_evolve), "evolve population path is wrong!" + with open(ROOT / opt.resume_evolve, errors="ignore") as f: + evolve_population = yaml.safe_load(f) + for value in evolve_population.values(): + value = np.array([value[k] for k in hyp_GA.keys()]) + initial_values.append(list(value)) + + # If not resuming from a previous checkpoint, generate initial values from .yaml files in opt.evolve_population + else: + yaml_files = [f for f in os.listdir(opt.evolve_population) if f.endswith(".yaml")] + for file_name in yaml_files: + with open(os.path.join(opt.evolve_population, file_name)) as yaml_file: + value = yaml.safe_load(yaml_file) + value = np.array([value[k] for k in hyp_GA.keys()]) + initial_values.append(list(value)) + + # Generate random values within the search space for the rest of the population + if initial_values is None: + population = [generate_individual(gene_ranges, len(hyp_GA)) for _ in range(pop_size)] + elif pop_size > 1: + population = [generate_individual(gene_ranges, len(hyp_GA)) for _ in range(pop_size - len(initial_values))] + for initial_value in initial_values: + population = [initial_value] + population + + # Run the genetic algorithm for a fixed number of generations + list_keys = list(hyp_GA.keys()) + for generation in range(opt.evolve): + if generation >= 1: + save_dict = {} + for i in range(len(population)): + little_dict = {list_keys[j]: float(population[i][j]) for j in range(len(population[i]))} + save_dict[f"gen{str(generation)}number{str(i)}"] = little_dict + + with open(save_dir / "evolve_population.yaml", "w") as outfile: + yaml.dump(save_dict, outfile, default_flow_style=False) + + # Adaptive elite size + elite_size = min_elite_size + int((max_elite_size - min_elite_size) * (generation / opt.evolve)) + # Evaluate the fitness of each individual in the population + fitness_scores = [] + for individual in population: + for key, value in zip(hyp_GA.keys(), individual): + hyp_GA[key] = value + hyp.update(hyp_GA) + results = train(hyp.copy(), opt, device, callbacks) + callbacks = Callbacks() + # Write mutation results + keys = ( + "metrics/precision", + "metrics/recall", + "metrics/mAP_0.5", + "metrics/mAP_0.5:0.95", + "val/box_loss", + "val/obj_loss", + "val/cls_loss", + ) + print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket) + fitness_scores.append(results[2]) + + # Select the fittest individuals for reproduction using adaptive tournament selection + selected_indices = [] + for _ in range(pop_size - elite_size): + # Adaptive tournament size + tournament_size = max( + max(2, tournament_size_min), + int(min(tournament_size_max, pop_size) - (generation / (opt.evolve / 10))), + ) + # Perform tournament selection to choose the best individual + tournament_indices = random.sample(range(pop_size), tournament_size) + tournament_fitness = [fitness_scores[j] for j in tournament_indices] + winner_index = tournament_indices[tournament_fitness.index(max(tournament_fitness))] + selected_indices.append(winner_index) + + # Add the elite individuals to the selected indices + elite_indices = [i for i in range(pop_size) if fitness_scores[i] in sorted(fitness_scores)[-elite_size:]] + selected_indices.extend(elite_indices) + # Create the next generation through crossover and mutation + next_generation = [] + for _ in range(pop_size): + parent1_index = selected_indices[random.randint(0, pop_size - 1)] + parent2_index = selected_indices[random.randint(0, pop_size - 1)] + # Adaptive crossover rate + crossover_rate = max( + crossover_rate_min, min(crossover_rate_max, crossover_rate_max - (generation / opt.evolve)) + ) + if random.uniform(0, 1) < crossover_rate: + crossover_point = random.randint(1, len(hyp_GA) - 1) + child = population[parent1_index][:crossover_point] + population[parent2_index][crossover_point:] + else: + child = population[parent1_index] + # Adaptive mutation rate + mutation_rate = max( + mutation_rate_min, min(mutation_rate_max, mutation_rate_max - (generation / opt.evolve)) + ) + for j in range(len(hyp_GA)): + if random.uniform(0, 1) < mutation_rate: + child[j] += random.uniform(-0.1, 0.1) + child[j] = min(max(child[j], gene_ranges[j][0]), gene_ranges[j][1]) + next_generation.append(child) + # Replace the old population with the new generation + population = next_generation + # Print the best solution found + best_index = fitness_scores.index(max(fitness_scores)) + best_individual = population[best_index] + print("Best solution found:", best_individual) + # Plot results + plot_evolve(evolve_csv) + LOGGER.info( + f'Hyperparameter evolution finished {opt.evolve} generations\n' + f"Results saved to {colorstr('bold', save_dir)}\n" + f'Usage example: $ python train.py --hyp {evolve_yaml}' + ) + + +def generate_individual(input_ranges, individual_length): + """Generates a list of random values within specified input ranges for each gene in the individual.""" + individual = [] + for i in range(individual_length): + lower_bound, upper_bound = input_ranges[i] + individual.append(random.uniform(lower_bound, upper_bound)) + return individual + + +def run(**kwargs): + """ + Executes YOLOv5 training with given options, overriding with any kwargs provided. + + Example: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt') + """ + opt = parse_opt(True) + for k, v in kwargs.items(): + setattr(opt, k, v) + main(opt) + return opt + + +if __name__ == "__main__": + opt = parse_opt() + main(opt) diff --git a/yolov5/tutorial.ipynb b/yolov5/tutorial.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d7953a06599fc10ff3985792357bff7589b4ebd5 --- /dev/null +++ b/yolov5/tutorial.ipynb @@ -0,0 +1,604 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "YOLOv5 Tutorial", + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "t6MPjfT5NrKQ" + }, + "source": [ + "
\n", + "\n", + " \n", + " \n", + "\n", + "[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)\n", + "\n", + " \"Run\n", + " \"Open\n", + " \"Open\n", + "\n", + "This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
We hope that the resources in this notebook will help you get the most out of YOLOv5. Please browse the YOLOv5 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7mGmQbAO5pQb" + }, + "source": [ + "# Setup\n", + "\n", + "Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wbvMlHd_QwMG", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e8225db4-e61d-4640-8b1f-8bfce3331cea" + }, + "source": [ + "!git clone https://github.com/ultralytics/yolov5 # clone\n", + "%cd yolov5\n", + "%pip install -qr requirements.txt comet_ml # install\n", + "\n", + "import torch\n", + "import utils\n", + "display = utils.notebook_init() # checks" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 23.3/166.8 GB disk)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4JnkELT0cIJg" + }, + "source": [ + "# 1. Detect\n", + "\n", + "`detect.py` runs YOLOv5 inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/detect`. Example inference sources are:\n", + "\n", + "```shell\n", + "python detect.py --source 0 # webcam\n", + " img.jpg # image\n", + " vid.mp4 # video\n", + " screen # screenshot\n", + " path/ # directory\n", + " 'path/*.jpg' # glob\n", + " 'https://youtu.be/LNwODJXcvt4' # YouTube\n", + " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", + "```" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zR9ZbuQCH7FX", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "284ef04b-1596-412f-88f6-948828dd2b49" + }, + "source": [ + "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n", + "# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n", + "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", + "\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\n", + "100% 14.1M/14.1M [00:00<00:00, 24.5MB/s]\n", + "\n", + "Fusing layers... \n", + "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n", + "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 41.5ms\n", + "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 60.0ms\n", + "Speed: 0.5ms pre-process, 50.8ms inference, 37.7ms NMS per image at shape (1, 3, 640, 640)\n", + "Results saved to \u001b[1mruns/detect/exp\u001b[0m\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hkAzDWJ7cWTr" + }, + "source": [ + "        \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0eq1SMWl6Sfn" + }, + "source": [ + "# 2. Validate\n", + "Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "WQPtK1QYVaD_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "cf7d52f0-281c-4c96-a488-79f5908f8426" + }, + "source": [ + "# Download COCO val\n", + "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n", + "!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 780M/780M [00:12<00:00, 66.6MB/s]\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "X58w8JLpMnjH", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3e234e05-ee8b-4ad1-b1a4-f6a55d5e4f3d" + }, + "source": [ + "# Validate YOLOv5s on COCO val\n", + "!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[34m\u001b[1mval: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False\n", + "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", + "\n", + "Fusing layers... \n", + "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:02<00:00, 2024.59it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n", + " Class Images Instances P R mAP50 mAP50-95: 100% 157/157 [01:25<00:00, 1.84it/s]\n", + " all 5000 36335 0.671 0.519 0.566 0.371\n", + "Speed: 0.1ms pre-process, 3.1ms inference, 2.3ms NMS per image at shape (32, 3, 640, 640)\n", + "\n", + "Evaluating pycocotools mAP... saving runs/val/exp/yolov5s_predictions.json...\n", + "loading annotations into memory...\n", + "Done (t=0.43s)\n", + "creating index...\n", + "index created!\n", + "Loading and preparing results...\n", + "DONE (t=5.32s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=78.89s).\n", + "Accumulating evaluation results...\n", + "DONE (t=14.51s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.572\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.402\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.211\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.423\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.311\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.516\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.566\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.722\n", + "Results saved to \u001b[1mruns/val/exp\u001b[0m\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZY2VXXXu74w5" + }, + "source": [ + "# 3. Train\n", + "\n", + "

\n", + "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n", + "

\n", + "\n", + "Train a YOLOv5s model on the [COCO128](https://www.kaggle.com/ultralytics/coco128) dataset with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`.\n", + "\n", + "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n", + "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n", + "- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n", + "- **Training Results** are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n", + "
\n", + "\n", + "A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n", + "\n", + "## Label a dataset on Roboflow (optional)\n", + "\n", + "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package." + ] + }, + { + "cell_type": "code", + "source": [ + "#@title Select YOLOv5 🚀 logger {run: 'auto'}\n", + "logger = 'Comet' #@param ['Comet', 'ClearML', 'TensorBoard']\n", + "\n", + "if logger == 'Comet':\n", + " %pip install -q comet_ml\n", + " import comet_ml; comet_ml.init()\n", + "elif logger == 'ClearML':\n", + " %pip install -q clearml\n", + " import clearml; clearml.browser_login()\n", + "elif logger == 'TensorBoard':\n", + " %load_ext tensorboard\n", + " %tensorboard --logdir runs/train" + ], + "metadata": { + "id": "i3oKtE4g-aNn" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "1NcFxRcFdJ_O", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "bbeeea2b-04fc-4185-aa64-258690495b5a" + }, + "source": [ + "# Train YOLOv5s on COCO128 for 3 epochs\n", + "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2023-04-09 14:11:38.063605: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-04-09 14:11:39.026661: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n", + "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", + "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n", + "\n", + "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n", + "\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n", + "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n", + "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n", + "\n", + "Dataset not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017']\n", + "Downloading https://ultralytics.com/assets/coco128.zip to coco128.zip...\n", + "100% 6.66M/6.66M [00:00<00:00, 75.6MB/s]\n", + "Dataset download success ✅ (0.6s), saved to \u001b[1m/content/datasets\u001b[0m\n", + "\n", + " from n params module arguments \n", + " 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n", + " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", + " 2 -1 1 18816 models.common.C3 [64, 64, 1] \n", + " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", + " 4 -1 2 115712 models.common.C3 [128, 128, 2] \n", + " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n", + " 6 -1 3 625152 models.common.C3 [256, 256, 3] \n", + " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n", + " 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n", + " 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n", + " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 12 [-1, 6] 1 0 models.common.Concat [1] \n", + " 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n", + " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", + " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 16 [-1, 4] 1 0 models.common.Concat [1] \n", + " 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n", + " 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n", + " 19 [-1, 14] 1 0 models.common.Concat [1] \n", + " 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n", + " 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n", + " 22 [-1, 10] 1 0 models.common.Concat [1] \n", + " 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n", + " 24 [17, 20, 23] 1 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n", + "Model summary: 214 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs\n", + "\n", + "Transferred 349/349 items from yolov5s.pt\n", + "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", + "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n", + "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1709.36it/s]\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128/labels/train2017.cache\n", + "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 264.35it/s]\n", + "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00 # 2. paste API key\n", + "python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n", + "```\n", + "To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n", + "\n", + "\n", + "\"Comet" + ], + "metadata": { + "id": "nWOsI5wJR1o3" + } + }, + { + "cell_type": "markdown", + "source": [ + "## ClearML Logging and Automation 🌟 NEW\n", + "\n", + "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n", + "\n", + "- `pip install clearml`\n", + "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n", + "\n", + "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n", + "\n", + "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n", + "\n", + "\n", + "\"ClearML" + ], + "metadata": { + "id": "Lay2WsTjNJzP" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-WPvRbS5Swl6" + }, + "source": [ + "## Local Logging\n", + "\n", + "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n", + "\n", + "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices.\n", + "\n", + "\"Local\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zelyeqbyt3GD" + }, + "source": [ + "# Environments\n", + "\n", + "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n", + "\n", + "- **Notebooks** with free GPU: \"Run \"Open \"Open\n", + "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n", + "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n", + "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \"Docker\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Qu7Iesl0p54" + }, + "source": [ + "# Status\n", + "\n", + "![YOLOv5 CI](https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg)\n", + "\n", + "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IEijrePND_2I" + }, + "source": [ + "# Appendix\n", + "\n", + "Additional content below." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "GMusP4OAxFu6" + }, + "source": [ + "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n", + "import torch\n", + "\n", + "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True, trust_repo=True) # or yolov5n - yolov5x6 or custom\n", + "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n", + "results = model(im) # inference\n", + "results.print() # or .show(), .save(), .crop(), .pandas(), etc." + ], + "execution_count": null, + "outputs": [] + } + ] +} diff --git a/yolov5/utils/__init__.py b/yolov5/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c7ece49fae10944b9174720a941745cc4af4d5a1 --- /dev/null +++ b/yolov5/utils/__init__.py @@ -0,0 +1,96 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""utils/initialization.""" + +import contextlib +import platform +import threading + + +def emojis(str=""): + """Returns an emoji-safe version of a string, stripped of emojis on Windows platforms.""" + return str.encode().decode("ascii", "ignore") if platform.system() == "Windows" else str + + +class TryExcept(contextlib.ContextDecorator): + # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager + def __init__(self, msg=""): + """Initializes TryExcept with an optional message, used as a decorator or context manager for error handling.""" + self.msg = msg + + def __enter__(self): + """Enter the runtime context related to this object for error handling with an optional message.""" + pass + + def __exit__(self, exc_type, value, traceback): + """Context manager exit method that prints an error message with emojis if an exception occurred, always returns + True. + """ + if value: + print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}")) + return True + + +def threaded(func): + """Decorator @threaded to run a function in a separate thread, returning the thread instance.""" + + def wrapper(*args, **kwargs): + """Runs the decorated function in a separate daemon thread and returns the thread instance.""" + thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True) + thread.start() + return thread + + return wrapper + + +def join_threads(verbose=False): + """ + Joins all daemon threads, optionally printing their names if verbose is True. + + Example: atexit.register(lambda: join_threads()) + """ + main_thread = threading.current_thread() + for t in threading.enumerate(): + if t is not main_thread: + if verbose: + print(f"Joining thread {t.name}") + t.join() + + +def notebook_init(verbose=True): + """Initializes notebook environment by checking requirements, cleaning up, and displaying system info.""" + print("Checking setup...") + + import os + import shutil + + from ultralytics.utils.checks import check_requirements + + from utils.general import check_font, is_colab + from utils.torch_utils import select_device # imports + + check_font() + + import psutil + + if check_requirements("wandb", install=False): + os.system("pip uninstall -y wandb") # eliminate unexpected account creation prompt with infinite hang + if is_colab(): + shutil.rmtree("/content/sample_data", ignore_errors=True) # remove colab /sample_data directory + + # System info + display = None + if verbose: + gb = 1 << 30 # bytes to GiB (1024 ** 3) + ram = psutil.virtual_memory().total + total, used, free = shutil.disk_usage("/") + with contextlib.suppress(Exception): # clear display if ipython is installed + from IPython import display + + display.clear_output() + s = f"({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)" + else: + s = "" + + select_device(newline=False) + print(emojis(f"Setup complete ✅ {s}")) + return display diff --git a/yolov5/utils/__pycache__/__init__.cpython-311.pyc b/yolov5/utils/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a29a96f4038e4111a7b3e1a91106812097ac8e1f Binary files /dev/null and b/yolov5/utils/__pycache__/__init__.cpython-311.pyc differ diff --git a/yolov5/utils/__pycache__/downloads.cpython-311.pyc b/yolov5/utils/__pycache__/downloads.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c61851ff23b8338026d234529bba15102deda549 Binary files /dev/null and b/yolov5/utils/__pycache__/downloads.cpython-311.pyc differ diff --git a/yolov5/utils/activations.py b/yolov5/utils/activations.py new file mode 100644 index 0000000000000000000000000000000000000000..47f0a998024e96ba9df2df81f6decd169dd59ba2 --- /dev/null +++ b/yolov5/utils/activations.py @@ -0,0 +1,126 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Activation functions.""" + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class SiLU(nn.Module): + @staticmethod + def forward(x): + """ + Applies the Sigmoid-weighted Linear Unit (SiLU) activation function. + + https://arxiv.org/pdf/1606.08415.pdf. + """ + return x * torch.sigmoid(x) + + +class Hardswish(nn.Module): + @staticmethod + def forward(x): + """ + Applies the Hardswish activation function, compatible with TorchScript, CoreML, and ONNX. + + Equivalent to x * F.hardsigmoid(x) + """ + return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0 # for TorchScript, CoreML and ONNX + + +class Mish(nn.Module): + """Mish activation https://github.com/digantamisra98/Mish.""" + + @staticmethod + def forward(x): + """Applies the Mish activation function, a smooth alternative to ReLU.""" + return x * F.softplus(x).tanh() + + +class MemoryEfficientMish(nn.Module): + class F(torch.autograd.Function): + @staticmethod + def forward(ctx, x): + """Applies the Mish activation function, a smooth ReLU alternative, to the input tensor `x`.""" + ctx.save_for_backward(x) + return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) + + @staticmethod + def backward(ctx, grad_output): + """Computes the gradient of the Mish activation function with respect to input `x`.""" + x = ctx.saved_tensors[0] + sx = torch.sigmoid(x) + fx = F.softplus(x).tanh() + return grad_output * (fx + x * sx * (1 - fx * fx)) + + def forward(self, x): + """Applies the Mish activation function to the input tensor `x`.""" + return self.F.apply(x) + + +class FReLU(nn.Module): + """FReLU activation https://arxiv.org/abs/2007.11824.""" + + def __init__(self, c1, k=3): # ch_in, kernel + """Initializes FReLU activation with channel `c1` and kernel size `k`.""" + super().__init__() + self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False) + self.bn = nn.BatchNorm2d(c1) + + def forward(self, x): + """ + Applies FReLU activation with max operation between input and BN-convolved input. + + https://arxiv.org/abs/2007.11824 + """ + return torch.max(x, self.bn(self.conv(x))) + + +class AconC(nn.Module): + """ + ACON activation (activate or not) function. + + AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter + See "Activate or Not: Learning Customized Activation" https://arxiv.org/pdf/2009.04759.pdf. + """ + + def __init__(self, c1): + """Initializes AconC with learnable parameters p1, p2, and beta for channel-wise activation control.""" + super().__init__() + self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.beta = nn.Parameter(torch.ones(1, c1, 1, 1)) + + def forward(self, x): + """Applies AconC activation function with learnable parameters for channel-wise control on input tensor x.""" + dpx = (self.p1 - self.p2) * x + return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x + + +class MetaAconC(nn.Module): + """ + ACON activation (activate or not) function. + + AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter + See "Activate or Not: Learning Customized Activation" https://arxiv.org/pdf/2009.04759.pdf. + """ + + def __init__(self, c1, k=1, s=1, r=16): + """Initializes MetaAconC with params: channel_in (c1), kernel size (k=1), stride (s=1), reduction (r=16).""" + super().__init__() + c2 = max(r, c1 // r) + self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True) + self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True) + # self.bn1 = nn.BatchNorm2d(c2) + # self.bn2 = nn.BatchNorm2d(c1) + + def forward(self, x): + """Applies a forward pass transforming input `x` using learnable parameters and sigmoid activation.""" + y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True) + # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891 + # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable + beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed + dpx = (self.p1 - self.p2) * x + return dpx * torch.sigmoid(beta * dpx) + self.p2 * x diff --git a/yolov5/utils/augmentations.py b/yolov5/utils/augmentations.py new file mode 100644 index 0000000000000000000000000000000000000000..4a6e441d7c45b7f2826e20a3b93bfe23d6ff26b9 --- /dev/null +++ b/yolov5/utils/augmentations.py @@ -0,0 +1,445 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Image augmentation functions.""" + +import math +import random + +import cv2 +import numpy as np +import torch +import torchvision.transforms as T +import torchvision.transforms.functional as TF + +from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy +from utils.metrics import bbox_ioa + +IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean +IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation + + +class Albumentations: + # YOLOv5 Albumentations class (optional, only used if package is installed) + def __init__(self, size=640): + """Initializes Albumentations class for optional data augmentation in YOLOv5 with specified input size.""" + self.transform = None + prefix = colorstr("albumentations: ") + try: + import albumentations as A + + check_version(A.__version__, "1.0.3", hard=True) # version requirement + + T = [ + A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0), + A.Blur(p=0.01), + A.MedianBlur(p=0.01), + A.ToGray(p=0.01), + A.CLAHE(p=0.01), + A.RandomBrightnessContrast(p=0.0), + A.RandomGamma(p=0.0), + A.ImageCompression(quality_lower=75, p=0.0), + ] # transforms + self.transform = A.Compose(T, bbox_params=A.BboxParams(format="yolo", label_fields=["class_labels"])) + + LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p)) + except ImportError: # package not installed, skip + pass + except Exception as e: + LOGGER.info(f"{prefix}{e}") + + def __call__(self, im, labels, p=1.0): + """Applies transformations to an image and labels with probability `p`, returning updated image and labels.""" + if self.transform and random.random() < p: + new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed + im, labels = new["image"], np.array([[c, *b] for c, b in zip(new["class_labels"], new["bboxes"])]) + return im, labels + + +def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False): + """ + Applies ImageNet normalization to RGB images in BCHW format, modifying them in-place if specified. + + Example: y = (x - mean) / std + """ + return TF.normalize(x, mean, std, inplace=inplace) + + +def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD): + """Reverses ImageNet normalization for BCHW format RGB images by applying `x = x * std + mean`.""" + for i in range(3): + x[:, i] = x[:, i] * std[i] + mean[i] + return x + + +def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5): + """Applies HSV color-space augmentation to an image with random gains for hue, saturation, and value.""" + if hgain or sgain or vgain: + r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains + hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) + dtype = im.dtype # uint8 + + x = np.arange(0, 256, dtype=r.dtype) + lut_hue = ((x * r[0]) % 180).astype(dtype) + lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) + lut_val = np.clip(x * r[2], 0, 255).astype(dtype) + + im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) + cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed + + +def hist_equalize(im, clahe=True, bgr=False): + """Equalizes image histogram, with optional CLAHE, for BGR or RGB image with shape (n,m,3) and range 0-255.""" + yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) + if clahe: + c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) + yuv[:, :, 0] = c.apply(yuv[:, :, 0]) + else: + yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram + return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB + + +def replicate(im, labels): + """ + Replicates half of the smallest object labels in an image for data augmentation. + + Returns augmented image and labels. + """ + h, w = im.shape[:2] + boxes = labels[:, 1:].astype(int) + x1, y1, x2, y2 = boxes.T + s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) + for i in s.argsort()[: round(s.size * 0.5)]: # smallest indices + x1b, y1b, x2b, y2b = boxes[i] + bh, bw = y2b - y1b, x2b - x1b + yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y + x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] + im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # im4[ymin:ymax, xmin:xmax] + labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) + + return im, labels + + +def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): + """Resizes and pads image to new_shape with stride-multiple constraints, returns resized image, ratio, padding.""" + shape = im.shape[:2] # current shape [height, width] + if isinstance(new_shape, int): + new_shape = (new_shape, new_shape) + + # Scale ratio (new / old) + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + if not scaleup: # only scale down, do not scale up (for better val mAP) + r = min(r, 1.0) + + # Compute padding + ratio = r, r # width, height ratios + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding + if auto: # minimum rectangle + dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding + elif scaleFill: # stretch + dw, dh = 0.0, 0.0 + new_unpad = (new_shape[1], new_shape[0]) + ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios + + dw /= 2 # divide padding into 2 sides + dh /= 2 + + if shape[::-1] != new_unpad: # resize + im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border + return im, ratio, (dw, dh) + + +def random_perspective( + im, targets=(), segments=(), degrees=10, translate=0.1, scale=0.1, shear=10, perspective=0.0, border=(0, 0) +): + # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10)) + # targets = [cls, xyxy] + + """Applies random perspective transformation to an image, modifying the image and corresponding labels.""" + height = im.shape[0] + border[0] * 2 # shape(h,w,c) + width = im.shape[1] + border[1] * 2 + + # Center + C = np.eye(3) + C[0, 2] = -im.shape[1] / 2 # x translation (pixels) + C[1, 2] = -im.shape[0] / 2 # y translation (pixels) + + # Perspective + P = np.eye(3) + P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) + P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) + + # Rotation and Scale + R = np.eye(3) + a = random.uniform(-degrees, degrees) + # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations + s = random.uniform(1 - scale, 1 + scale) + # s = 2 ** random.uniform(-scale, scale) + R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) + + # Shear + S = np.eye(3) + S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) + S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) + + # Translation + T = np.eye(3) + T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) + T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) + + # Combined rotation matrix + M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT + if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed + if perspective: + im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) + else: # affine + im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) + + # Visualize + # import matplotlib.pyplot as plt + # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() + # ax[0].imshow(im[:, :, ::-1]) # base + # ax[1].imshow(im2[:, :, ::-1]) # warped + + # Transform label coordinates + n = len(targets) + if n: + use_segments = any(x.any() for x in segments) and len(segments) == n + new = np.zeros((n, 4)) + if use_segments: # warp segments + segments = resample_segments(segments) # upsample + for i, segment in enumerate(segments): + xy = np.ones((len(segment), 3)) + xy[:, :2] = segment + xy = xy @ M.T # transform + xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine + + # clip + new[i] = segment2box(xy, width, height) + + else: # warp boxes + xy = np.ones((n * 4, 3)) + xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 + xy = xy @ M.T # transform + xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine + + # create new boxes + x = xy[:, [0, 2, 4, 6]] + y = xy[:, [1, 3, 5, 7]] + new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T + + # clip + new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) + new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) + + # filter candidates + i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) + targets = targets[i] + targets[:, 1:5] = new[i] + + return im, targets + + +def copy_paste(im, labels, segments, p=0.5): + """ + Applies Copy-Paste augmentation by flipping and merging segments and labels on an image. + + Details at https://arxiv.org/abs/2012.07177. + """ + n = len(segments) + if p and n: + h, w, c = im.shape # height, width, channels + im_new = np.zeros(im.shape, np.uint8) + for j in random.sample(range(n), k=round(p * n)): + l, s = labels[j], segments[j] + box = w - l[3], l[2], w - l[1], l[4] + ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area + if (ioa < 0.30).all(): # allow 30% obscuration of existing labels + labels = np.concatenate((labels, [[l[0], *box]]), 0) + segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1)) + cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (1, 1, 1), cv2.FILLED) + + result = cv2.flip(im, 1) # augment segments (flip left-right) + i = cv2.flip(im_new, 1).astype(bool) + im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug + + return im, labels, segments + + +def cutout(im, labels, p=0.5): + """ + Applies cutout augmentation to an image with optional label adjustment, using random masks of varying sizes. + + Details at https://arxiv.org/abs/1708.04552. + """ + if random.random() < p: + h, w = im.shape[:2] + scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction + for s in scales: + mask_h = random.randint(1, int(h * s)) # create random masks + mask_w = random.randint(1, int(w * s)) + + # box + xmin = max(0, random.randint(0, w) - mask_w // 2) + ymin = max(0, random.randint(0, h) - mask_h // 2) + xmax = min(w, xmin + mask_w) + ymax = min(h, ymin + mask_h) + + # apply random color mask + im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] + + # return unobscured labels + if len(labels) and s > 0.03: + box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) + ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h)) # intersection over area + labels = labels[ioa < 0.60] # remove >60% obscured labels + + return labels + + +def mixup(im, labels, im2, labels2): + """ + Applies MixUp augmentation by blending images and labels. + + See https://arxiv.org/pdf/1710.09412.pdf for details. + """ + r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 + im = (im * r + im2 * (1 - r)).astype(np.uint8) + labels = np.concatenate((labels, labels2), 0) + return im, labels + + +def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): + """ + Filters bounding box candidates by minimum width-height threshold `wh_thr` (pixels), aspect ratio threshold + `ar_thr`, and area ratio threshold `area_thr`. + + box1(4,n) is before augmentation, box2(4,n) is after augmentation. + """ + w1, h1 = box1[2] - box1[0], box1[3] - box1[1] + w2, h2 = box2[2] - box2[0], box2[3] - box2[1] + ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio + return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates + + +def classify_albumentations( + augment=True, + size=224, + scale=(0.08, 1.0), + ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33 + hflip=0.5, + vflip=0.0, + jitter=0.4, + mean=IMAGENET_MEAN, + std=IMAGENET_STD, + auto_aug=False, +): + # YOLOv5 classification Albumentations (optional, only used if package is installed) + """Sets up and returns Albumentations transforms for YOLOv5 classification tasks depending on augmentation + settings. + """ + prefix = colorstr("albumentations: ") + try: + import albumentations as A + from albumentations.pytorch import ToTensorV2 + + check_version(A.__version__, "1.0.3", hard=True) # version requirement + if augment: # Resize and crop + T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)] + if auto_aug: + # TODO: implement AugMix, AutoAug & RandAug in albumentation + LOGGER.info(f"{prefix}auto augmentations are currently not supported") + else: + if hflip > 0: + T += [A.HorizontalFlip(p=hflip)] + if vflip > 0: + T += [A.VerticalFlip(p=vflip)] + if jitter > 0: + color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, saturation, 0 hue + T += [A.ColorJitter(*color_jitter, 0)] + else: # Use fixed crop for eval set (reproducibility) + T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)] + T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor + LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p)) + return A.Compose(T) + + except ImportError: # package not installed, skip + LOGGER.warning(f"{prefix}⚠️ not found, install with `pip install albumentations` (recommended)") + except Exception as e: + LOGGER.info(f"{prefix}{e}") + + +def classify_transforms(size=224): + """Applies a series of transformations including center crop, ToTensor, and normalization for classification.""" + assert isinstance(size, int), f"ERROR: classify_transforms size {size} must be integer, not (list, tuple)" + # T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) + return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) + + +class LetterBox: + # YOLOv5 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) + def __init__(self, size=(640, 640), auto=False, stride=32): + """Initializes a LetterBox object for YOLOv5 image preprocessing with optional auto sizing and stride + adjustment. + """ + super().__init__() + self.h, self.w = (size, size) if isinstance(size, int) else size + self.auto = auto # pass max size integer, automatically solve for short side using stride + self.stride = stride # used with auto + + def __call__(self, im): + """ + Resizes and pads input image `im` (HWC format) to specified dimensions, maintaining aspect ratio. + + im = np.array HWC + """ + imh, imw = im.shape[:2] + r = min(self.h / imh, self.w / imw) # ratio of new/old + h, w = round(imh * r), round(imw * r) # resized image + hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w + top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1) + im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype) + im_out[top : top + h, left : left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR) + return im_out + + +class CenterCrop: + # YOLOv5 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()]) + def __init__(self, size=640): + """Initializes CenterCrop for image preprocessing, accepting single int or tuple for size, defaults to 640.""" + super().__init__() + self.h, self.w = (size, size) if isinstance(size, int) else size + + def __call__(self, im): + """ + Applies center crop to the input image and resizes it to a specified size, maintaining aspect ratio. + + im = np.array HWC + """ + imh, imw = im.shape[:2] + m = min(imh, imw) # min dimension + top, left = (imh - m) // 2, (imw - m) // 2 + return cv2.resize(im[top : top + m, left : left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR) + + +class ToTensor: + # YOLOv5 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) + def __init__(self, half=False): + """Initializes ToTensor for YOLOv5 image preprocessing, with optional half precision (half=True for FP16).""" + super().__init__() + self.half = half + + def __call__(self, im): + """ + Converts BGR np.array image from HWC to RGB CHW format, and normalizes to [0, 1], with support for FP16 if + `half=True`. + + im = np.array HWC in BGR order + """ + im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous + im = torch.from_numpy(im) # to torch + im = im.half() if self.half else im.float() # uint8 to fp16/32 + im /= 255.0 # 0-255 to 0.0-1.0 + return im diff --git a/yolov5/utils/autoanchor.py b/yolov5/utils/autoanchor.py new file mode 100644 index 0000000000000000000000000000000000000000..00eee2eb776d1d2357b3c74557f40b42b67927c8 --- /dev/null +++ b/yolov5/utils/autoanchor.py @@ -0,0 +1,175 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""AutoAnchor utils.""" + +import random + +import numpy as np +import torch +import yaml +from tqdm import tqdm + +from utils import TryExcept +from utils.general import LOGGER, TQDM_BAR_FORMAT, colorstr + +PREFIX = colorstr("AutoAnchor: ") + + +def check_anchor_order(m): + """Checks and corrects anchor order against stride in YOLOv5 Detect() module if necessary.""" + a = m.anchors.prod(-1).mean(-1).view(-1) # mean anchor area per output layer + da = a[-1] - a[0] # delta a + ds = m.stride[-1] - m.stride[0] # delta s + if da and (da.sign() != ds.sign()): # same order + LOGGER.info(f"{PREFIX}Reversing anchor order") + m.anchors[:] = m.anchors.flip(0) + + +@TryExcept(f"{PREFIX}ERROR") +def check_anchors(dataset, model, thr=4.0, imgsz=640): + """Evaluates anchor fit to dataset and adjusts if necessary, supporting customizable threshold and image size.""" + m = model.module.model[-1] if hasattr(model, "module") else model.model[-1] # Detect() + shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) + scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale + wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh + + def metric(k): # compute metric + """Computes ratio metric, anchors above threshold, and best possible recall for YOLOv5 anchor evaluation.""" + r = wh[:, None] / k[None] + x = torch.min(r, 1 / r).min(2)[0] # ratio metric + best = x.max(1)[0] # best_x + aat = (x > 1 / thr).float().sum(1).mean() # anchors above threshold + bpr = (best > 1 / thr).float().mean() # best possible recall + return bpr, aat + + stride = m.stride.to(m.anchors.device).view(-1, 1, 1) # model strides + anchors = m.anchors.clone() * stride # current anchors + bpr, aat = metric(anchors.cpu().view(-1, 2)) + s = f"\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). " + if bpr > 0.98: # threshold to recompute + LOGGER.info(f"{s}Current anchors are a good fit to dataset ✅") + else: + LOGGER.info(f"{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...") + na = m.anchors.numel() // 2 # number of anchors + anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) + new_bpr = metric(anchors)[0] + if new_bpr > bpr: # replace anchors + anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) + m.anchors[:] = anchors.clone().view_as(m.anchors) + check_anchor_order(m) # must be in pixel-space (not grid-space) + m.anchors /= stride + s = f"{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)" + else: + s = f"{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)" + LOGGER.info(s) + + +def kmean_anchors(dataset="./data/coco128.yaml", n=9, img_size=640, thr=4.0, gen=1000, verbose=True): + """ + Creates kmeans-evolved anchors from training dataset. + + Arguments: + dataset: path to data.yaml, or a loaded dataset + n: number of anchors + img_size: image size used for training + thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 + gen: generations to evolve anchors using genetic algorithm + verbose: print all results + + Return: + k: kmeans evolved anchors + + Usage: + from utils.autoanchor import *; _ = kmean_anchors() + """ + from scipy.cluster.vq import kmeans + + npr = np.random + thr = 1 / thr + + def metric(k, wh): # compute metrics + """Computes ratio metric, anchors above threshold, and best possible recall for YOLOv5 anchor evaluation.""" + r = wh[:, None] / k[None] + x = torch.min(r, 1 / r).min(2)[0] # ratio metric + # x = wh_iou(wh, torch.tensor(k)) # iou metric + return x, x.max(1)[0] # x, best_x + + def anchor_fitness(k): # mutation fitness + """Evaluates fitness of YOLOv5 anchors by computing recall and ratio metrics for an anchor evolution process.""" + _, best = metric(torch.tensor(k, dtype=torch.float32), wh) + return (best * (best > thr).float()).mean() # fitness + + def print_results(k, verbose=True): + """Sorts and logs kmeans-evolved anchor metrics and best possible recall values for YOLOv5 anchor evaluation.""" + k = k[np.argsort(k.prod(1))] # sort small to large + x, best = metric(k, wh0) + bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr + s = ( + f"{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n" + f"{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, " + f"past_thr={x[x > thr].mean():.3f}-mean: " + ) + for x in k: + s += "%i,%i, " % (round(x[0]), round(x[1])) + if verbose: + LOGGER.info(s[:-2]) + return k + + if isinstance(dataset, str): # *.yaml file + with open(dataset, errors="ignore") as f: + data_dict = yaml.safe_load(f) # model dict + from utils.dataloaders import LoadImagesAndLabels + + dataset = LoadImagesAndLabels(data_dict["train"], augment=True, rect=True) + + # Get label wh + shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) + wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh + + # Filter + i = (wh0 < 3.0).any(1).sum() + if i: + LOGGER.info(f"{PREFIX}WARNING ⚠️ Extremely small objects found: {i} of {len(wh0)} labels are <3 pixels in size") + wh = wh0[(wh0 >= 2.0).any(1)].astype(np.float32) # filter > 2 pixels + # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 + + # Kmeans init + try: + LOGGER.info(f"{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...") + assert n <= len(wh) # apply overdetermined constraint + s = wh.std(0) # sigmas for whitening + k = kmeans(wh / s, n, iter=30)[0] * s # points + assert n == len(k) # kmeans may return fewer points than requested if wh is insufficient or too similar + except Exception: + LOGGER.warning(f"{PREFIX}WARNING ⚠️ switching strategies from kmeans to random init") + k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size # random init + wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0)) + k = print_results(k, verbose=False) + + # Plot + # k, d = [None] * 20, [None] * 20 + # for i in tqdm(range(1, 21)): + # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance + # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True) + # ax = ax.ravel() + # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') + # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh + # ax[0].hist(wh[wh[:, 0]<100, 0],400) + # ax[1].hist(wh[wh[:, 1]<100, 1],400) + # fig.savefig('wh.png', dpi=200) + + # Evolve + f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma + pbar = tqdm(range(gen), bar_format=TQDM_BAR_FORMAT) # progress bar + for _ in pbar: + v = np.ones(sh) + while (v == 1).all(): # mutate until a change occurs (prevent duplicates) + v = ((npr.random(sh) < mp) * random.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0) + kg = (k.copy() * v).clip(min=2.0) + fg = anchor_fitness(kg) + if fg > f: + f, k = fg, kg.copy() + pbar.desc = f"{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}" + if verbose: + print_results(k, verbose) + + return print_results(k).astype(np.float32) diff --git a/yolov5/utils/autobatch.py b/yolov5/utils/autobatch.py new file mode 100644 index 0000000000000000000000000000000000000000..08a0de841a980ccee331fad8c660643bef656bce --- /dev/null +++ b/yolov5/utils/autobatch.py @@ -0,0 +1,70 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Auto-batch utils.""" + +from copy import deepcopy + +import numpy as np +import torch + +from utils.general import LOGGER, colorstr +from utils.torch_utils import profile + + +def check_train_batch_size(model, imgsz=640, amp=True): + """Checks and computes optimal training batch size for YOLOv5 model, given image size and AMP setting.""" + with torch.cuda.amp.autocast(amp): + return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size + + +def autobatch(model, imgsz=640, fraction=0.8, batch_size=16): + """Estimates optimal YOLOv5 batch size using `fraction` of CUDA memory.""" + # Usage: + # import torch + # from utils.autobatch import autobatch + # model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False) + # print(autobatch(model)) + + # Check device + prefix = colorstr("AutoBatch: ") + LOGGER.info(f"{prefix}Computing optimal batch size for --imgsz {imgsz}") + device = next(model.parameters()).device # get model device + if device.type == "cpu": + LOGGER.info(f"{prefix}CUDA not detected, using default CPU batch-size {batch_size}") + return batch_size + if torch.backends.cudnn.benchmark: + LOGGER.info(f"{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}") + return batch_size + + # Inspect CUDA memory + gb = 1 << 30 # bytes to GiB (1024 ** 3) + d = str(device).upper() # 'CUDA:0' + properties = torch.cuda.get_device_properties(device) # device properties + t = properties.total_memory / gb # GiB total + r = torch.cuda.memory_reserved(device) / gb # GiB reserved + a = torch.cuda.memory_allocated(device) / gb # GiB allocated + f = t - (r + a) # GiB free + LOGGER.info(f"{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free") + + # Profile batch sizes + batch_sizes = [1, 2, 4, 8, 16] + try: + img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes] + results = profile(img, model, n=3, device=device) + except Exception as e: + LOGGER.warning(f"{prefix}{e}") + + # Fit a solution + y = [x[2] for x in results if x] # memory [2] + p = np.polyfit(batch_sizes[: len(y)], y, deg=1) # first degree polynomial fit + b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size) + if None in results: # some sizes failed + i = results.index(None) # first fail index + if b >= batch_sizes[i]: # y intercept above failure point + b = batch_sizes[max(i - 1, 0)] # select prior safe point + if b < 1 or b > 1024: # b outside of safe range + b = batch_size + LOGGER.warning(f"{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.") + + fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted + LOGGER.info(f"{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅") + return b diff --git a/yolov5/utils/aws/__init__.py b/yolov5/utils/aws/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/yolov5/utils/aws/mime.sh b/yolov5/utils/aws/mime.sh new file mode 100644 index 0000000000000000000000000000000000000000..c319a83cfbdf09bea634c3bd9fca737c0b1dd505 --- /dev/null +++ b/yolov5/utils/aws/mime.sh @@ -0,0 +1,26 @@ +# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ +# This script will run on every instance restart, not only on first start +# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- + +Content-Type: multipart/mixed; boundary="//" +MIME-Version: 1.0 + +--// +Content-Type: text/cloud-config; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="cloud-config.txt" + +#cloud-config +cloud_final_modules: +- [scripts-user, always] + +--// +Content-Type: text/x-shellscript; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="userdata.txt" + +#!/bin/bash +# --- paste contents of userdata.sh here --- +--// diff --git a/yolov5/utils/aws/resume.py b/yolov5/utils/aws/resume.py new file mode 100644 index 0000000000000000000000000000000000000000..ea432a1615ca74ca2ac6f962396db2b7ca3b5082 --- /dev/null +++ b/yolov5/utils/aws/resume.py @@ -0,0 +1,41 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +# Resume all interrupted trainings in yolov5/ dir including DDP trainings +# Usage: $ python utils/aws/resume.py + +import os +import sys +from pathlib import Path + +import torch +import yaml + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[2] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH + +port = 0 # --master_port +path = Path("").resolve() +for last in path.rglob("*/**/last.pt"): + ckpt = torch.load(last) + if ckpt["optimizer"] is None: + continue + + # Load opt.yaml + with open(last.parent.parent / "opt.yaml", errors="ignore") as f: + opt = yaml.safe_load(f) + + # Get device count + d = opt["device"].split(",") # devices + nd = len(d) # number of devices + ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel + + if ddp: # multi-GPU + port += 1 + cmd = f"python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}" + else: # single-GPU + cmd = f"python train.py --resume {last}" + + cmd += " > /dev/null 2>&1 &" # redirect output to dev/null and run in daemon thread + print(cmd) + os.system(cmd) diff --git a/yolov5/utils/aws/userdata.sh b/yolov5/utils/aws/userdata.sh new file mode 100644 index 0000000000000000000000000000000000000000..5fc1332ac1b0d1794cf8f8c5f6918059ae5dc381 --- /dev/null +++ b/yolov5/utils/aws/userdata.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html +# This script will run only once on first instance start (for a re-start script see mime.sh) +# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir +# Use >300 GB SSD + +cd home/ubuntu +if [ ! -d yolov5 ]; then + echo "Running first-time script." # install dependencies, download COCO, pull Docker + git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5 + cd yolov5 + bash data/scripts/get_coco.sh && echo "COCO done." & + sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & + python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & + wait && echo "All tasks done." # finish background tasks +else + echo "Running re-start script." # resume interrupted runs + i=0 + list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' + while IFS= read -r id; do + ((i++)) + echo "restarting container $i: $id" + sudo docker start $id + # sudo docker exec -it $id python train.py --resume # single-GPU + sudo docker exec -d $id python utils/aws/resume.py # multi-scenario + done <<<"$list" +fi diff --git a/yolov5/utils/callbacks.py b/yolov5/utils/callbacks.py new file mode 100644 index 0000000000000000000000000000000000000000..0a0bcbdb2b965cc07bdfde933483b15e3ef88b4a --- /dev/null +++ b/yolov5/utils/callbacks.py @@ -0,0 +1,73 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Callback utils.""" + +import threading + + +class Callbacks: + """Handles all registered callbacks for YOLOv5 Hooks.""" + + def __init__(self): + """Initializes a Callbacks object to manage registered YOLOv5 training event hooks.""" + self._callbacks = { + "on_pretrain_routine_start": [], + "on_pretrain_routine_end": [], + "on_train_start": [], + "on_train_epoch_start": [], + "on_train_batch_start": [], + "optimizer_step": [], + "on_before_zero_grad": [], + "on_train_batch_end": [], + "on_train_epoch_end": [], + "on_val_start": [], + "on_val_batch_start": [], + "on_val_image_end": [], + "on_val_batch_end": [], + "on_val_end": [], + "on_fit_epoch_end": [], # fit = train + val + "on_model_save": [], + "on_train_end": [], + "on_params_update": [], + "teardown": [], + } + self.stop_training = False # set True to interrupt training + + def register_action(self, hook, name="", callback=None): + """ + Register a new action to a callback hook. + + Args: + hook: The callback hook name to register the action to + name: The name of the action for later reference + callback: The callback to fire + """ + assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" + assert callable(callback), f"callback '{callback}' is not callable" + self._callbacks[hook].append({"name": name, "callback": callback}) + + def get_registered_actions(self, hook=None): + """ + Returns all the registered actions by callback hook. + + Args: + hook: The name of the hook to check, defaults to all + """ + return self._callbacks[hook] if hook else self._callbacks + + def run(self, hook, *args, thread=False, **kwargs): + """ + Loop through the registered actions and fire all callbacks on main thread. + + Args: + hook: The name of the hook to check, defaults to all + args: Arguments to receive from YOLOv5 + thread: (boolean) Run callbacks in daemon thread + kwargs: Keyword Arguments to receive from YOLOv5 + """ + + assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" + for logger in self._callbacks[hook]: + if thread: + threading.Thread(target=logger["callback"], args=args, kwargs=kwargs, daemon=True).start() + else: + logger["callback"](*args, **kwargs) diff --git a/yolov5/utils/dataloaders.py b/yolov5/utils/dataloaders.py new file mode 100644 index 0000000000000000000000000000000000000000..21308f0cedbd4ffa5453d6a89af8fcf1af9960ea --- /dev/null +++ b/yolov5/utils/dataloaders.py @@ -0,0 +1,1374 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Dataloaders and dataset utils.""" + +import contextlib +import glob +import hashlib +import json +import math +import os +import random +import shutil +import time +from itertools import repeat +from multiprocessing.pool import Pool, ThreadPool +from pathlib import Path +from threading import Thread +from urllib.parse import urlparse + +import numpy as np +import psutil +import torch +import torch.nn.functional as F +import torchvision +import yaml +from PIL import ExifTags, Image, ImageOps +from torch.utils.data import DataLoader, Dataset, dataloader, distributed +from tqdm import tqdm + +from utils.augmentations import ( + Albumentations, + augment_hsv, + classify_albumentations, + classify_transforms, + copy_paste, + letterbox, + mixup, + random_perspective, +) +from utils.general import ( + DATASETS_DIR, + LOGGER, + NUM_THREADS, + TQDM_BAR_FORMAT, + check_dataset, + check_requirements, + check_yaml, + clean_str, + cv2, + is_colab, + is_kaggle, + segments2boxes, + unzip_file, + xyn2xy, + xywh2xyxy, + xywhn2xyxy, + xyxy2xywhn, +) +from utils.torch_utils import torch_distributed_zero_first + +# Parameters +HELP_URL = "See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data" +IMG_FORMATS = "bmp", "dng", "jpeg", "jpg", "mpo", "png", "tif", "tiff", "webp", "pfm" # include image suffixes +VID_FORMATS = "asf", "avi", "gif", "m4v", "mkv", "mov", "mp4", "mpeg", "mpg", "ts", "wmv" # include video suffixes +LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv("RANK", -1)) +WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) +PIN_MEMORY = str(os.getenv("PIN_MEMORY", True)).lower() == "true" # global pin_memory for dataloaders + +# Get orientation exif tag +for orientation in ExifTags.TAGS.keys(): + if ExifTags.TAGS[orientation] == "Orientation": + break + + +def get_hash(paths): + """Generates a single SHA256 hash for a list of file or directory paths by combining their sizes and paths.""" + size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes + h = hashlib.sha256(str(size).encode()) # hash sizes + h.update("".join(paths).encode()) # hash paths + return h.hexdigest() # return hash + + +def exif_size(img): + """Returns corrected PIL image size (width, height) considering EXIF orientation.""" + s = img.size # (width, height) + with contextlib.suppress(Exception): + rotation = dict(img._getexif().items())[orientation] + if rotation in [6, 8]: # rotation 270 or 90 + s = (s[1], s[0]) + return s + + +def exif_transpose(image): + """ + Transpose a PIL image accordingly if it has an EXIF Orientation tag. + Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose() + + :param image: The image to transpose. + :return: An image. + """ + exif = image.getexif() + orientation = exif.get(0x0112, 1) # default 1 + if orientation > 1: + method = { + 2: Image.FLIP_LEFT_RIGHT, + 3: Image.ROTATE_180, + 4: Image.FLIP_TOP_BOTTOM, + 5: Image.TRANSPOSE, + 6: Image.ROTATE_270, + 7: Image.TRANSVERSE, + 8: Image.ROTATE_90, + }.get(orientation) + if method is not None: + image = image.transpose(method) + del exif[0x0112] + image.info["exif"] = exif.tobytes() + return image + + +def seed_worker(worker_id): + """ + Sets the seed for a dataloader worker to ensure reproducibility, based on PyTorch's randomness notes. + + See https://pytorch.org/docs/stable/notes/randomness.html#dataloader. + """ + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + random.seed(worker_seed) + + +# Inherit from DistributedSampler and override iterator +# https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py +class SmartDistributedSampler(distributed.DistributedSampler): + def __iter__(self): + """Yields indices for distributed data sampling, shuffled deterministically based on epoch and seed.""" + g = torch.Generator() + g.manual_seed(self.seed + self.epoch) + + # determine the eventual size (n) of self.indices (DDP indices) + n = int((len(self.dataset) - self.rank - 1) / self.num_replicas) + 1 # num_replicas == WORLD_SIZE + idx = torch.randperm(n, generator=g) + if not self.shuffle: + idx = idx.sort()[0] + + idx = idx.tolist() + if self.drop_last: + idx = idx[: self.num_samples] + else: + padding_size = self.num_samples - len(idx) + if padding_size <= len(idx): + idx += idx[:padding_size] + else: + idx += (idx * math.ceil(padding_size / len(idx)))[:padding_size] + + return iter(idx) + + +def create_dataloader( + path, + imgsz, + batch_size, + stride, + single_cls=False, + hyp=None, + augment=False, + cache=False, + pad=0.0, + rect=False, + rank=-1, + workers=8, + image_weights=False, + quad=False, + prefix="", + shuffle=False, + seed=0, +): + """Creates and returns a configured DataLoader instance for loading and processing image datasets.""" + if rect and shuffle: + LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False") + shuffle = False + with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP + dataset = LoadImagesAndLabels( + path, + imgsz, + batch_size, + augment=augment, # augmentation + hyp=hyp, # hyperparameters + rect=rect, # rectangular batches + cache_images=cache, + single_cls=single_cls, + stride=int(stride), + pad=pad, + image_weights=image_weights, + prefix=prefix, + rank=rank, + ) + + batch_size = min(batch_size, len(dataset)) + nd = torch.cuda.device_count() # number of CUDA devices + nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers + sampler = None if rank == -1 else SmartDistributedSampler(dataset, shuffle=shuffle) + loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates + generator = torch.Generator() + generator.manual_seed(6148914691236517205 + seed + RANK) + return loader( + dataset, + batch_size=batch_size, + shuffle=shuffle and sampler is None, + num_workers=nw, + sampler=sampler, + pin_memory=PIN_MEMORY, + collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn, + worker_init_fn=seed_worker, + generator=generator, + ), dataset + + +class InfiniteDataLoader(dataloader.DataLoader): + """ + Dataloader that reuses workers. + + Uses same syntax as vanilla DataLoader + """ + + def __init__(self, *args, **kwargs): + """Initializes an InfiniteDataLoader that reuses workers with standard DataLoader syntax, augmenting with a + repeating sampler. + """ + super().__init__(*args, **kwargs) + object.__setattr__(self, "batch_sampler", _RepeatSampler(self.batch_sampler)) + self.iterator = super().__iter__() + + def __len__(self): + """Returns the length of the batch sampler's sampler in the InfiniteDataLoader.""" + return len(self.batch_sampler.sampler) + + def __iter__(self): + """Yields batches of data indefinitely in a loop by resetting the sampler when exhausted.""" + for _ in range(len(self)): + yield next(self.iterator) + + +class _RepeatSampler: + """ + Sampler that repeats forever. + + Args: + sampler (Sampler) + """ + + def __init__(self, sampler): + """Initializes a perpetual sampler wrapping a provided `Sampler` instance for endless data iteration.""" + self.sampler = sampler + + def __iter__(self): + """Returns an infinite iterator over the dataset by repeatedly yielding from the given sampler.""" + while True: + yield from iter(self.sampler) + + +class LoadScreenshots: + # YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"` + def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None): + """ + Initializes a screenshot dataloader for YOLOv5 with specified source region, image size, stride, auto, and + transforms. + + Source = [screen_number left top width height] (pixels) + """ + check_requirements("mss") + import mss + + source, *params = source.split() + self.screen, left, top, width, height = 0, None, None, None, None # default to full screen 0 + if len(params) == 1: + self.screen = int(params[0]) + elif len(params) == 4: + left, top, width, height = (int(x) for x in params) + elif len(params) == 5: + self.screen, left, top, width, height = (int(x) for x in params) + self.img_size = img_size + self.stride = stride + self.transforms = transforms + self.auto = auto + self.mode = "stream" + self.frame = 0 + self.sct = mss.mss() + + # Parse monitor shape + monitor = self.sct.monitors[self.screen] + self.top = monitor["top"] if top is None else (monitor["top"] + top) + self.left = monitor["left"] if left is None else (monitor["left"] + left) + self.width = width or monitor["width"] + self.height = height or monitor["height"] + self.monitor = {"left": self.left, "top": self.top, "width": self.width, "height": self.height} + + def __iter__(self): + """Iterates over itself, enabling use in loops and iterable contexts.""" + return self + + def __next__(self): + """Captures and returns the next screen frame as a BGR numpy array, cropping to only the first three channels + from BGRA. + """ + im0 = np.array(self.sct.grab(self.monitor))[:, :, :3] # [:, :, :3] BGRA to BGR + s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: " + + if self.transforms: + im = self.transforms(im0) # transforms + else: + im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize + im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB + im = np.ascontiguousarray(im) # contiguous + self.frame += 1 + return str(self.screen), im, im0, None, s # screen, img, original img, im0s, s + + +class LoadImages: + """YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`""" + + def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): + """Initializes YOLOv5 loader for images/videos, supporting glob patterns, directories, and lists of paths.""" + if isinstance(path, str) and Path(path).suffix == ".txt": # *.txt file with img/vid/dir on each line + path = Path(path).read_text().rsplit() + files = [] + for p in sorted(path) if isinstance(path, (list, tuple)) else [path]: + p = str(Path(p).resolve()) + if "*" in p: + files.extend(sorted(glob.glob(p, recursive=True))) # glob + elif os.path.isdir(p): + files.extend(sorted(glob.glob(os.path.join(p, "*.*")))) # dir + elif os.path.isfile(p): + files.append(p) # files + else: + raise FileNotFoundError(f"{p} does not exist") + + images = [x for x in files if x.split(".")[-1].lower() in IMG_FORMATS] + videos = [x for x in files if x.split(".")[-1].lower() in VID_FORMATS] + ni, nv = len(images), len(videos) + + self.img_size = img_size + self.stride = stride + self.files = images + videos + self.nf = ni + nv # number of files + self.video_flag = [False] * ni + [True] * nv + self.mode = "image" + self.auto = auto + self.transforms = transforms # optional + self.vid_stride = vid_stride # video frame-rate stride + if any(videos): + self._new_video(videos[0]) # new video + else: + self.cap = None + assert self.nf > 0, ( + f"No images or videos found in {p}. " + f"Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}" + ) + + def __iter__(self): + """Initializes iterator by resetting count and returns the iterator object itself.""" + self.count = 0 + return self + + def __next__(self): + """Advances to the next file in the dataset, raising StopIteration if at the end.""" + if self.count == self.nf: + raise StopIteration + path = self.files[self.count] + + if self.video_flag[self.count]: + # Read video + self.mode = "video" + for _ in range(self.vid_stride): + self.cap.grab() + ret_val, im0 = self.cap.retrieve() + while not ret_val: + self.count += 1 + self.cap.release() + if self.count == self.nf: # last video + raise StopIteration + path = self.files[self.count] + self._new_video(path) + ret_val, im0 = self.cap.read() + + self.frame += 1 + # im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False + s = f"video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: " + + else: + # Read image + self.count += 1 + im0 = cv2.imread(path) # BGR + assert im0 is not None, f"Image Not Found {path}" + s = f"image {self.count}/{self.nf} {path}: " + + if self.transforms: + im = self.transforms(im0) # transforms + else: + im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize + im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB + im = np.ascontiguousarray(im) # contiguous + + return path, im, im0, self.cap, s + + def _new_video(self, path): + """Initializes a new video capture object with path, frame count adjusted by stride, and orientation + metadata. + """ + self.frame = 0 + self.cap = cv2.VideoCapture(path) + self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride) + self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META)) # rotation degrees + # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0) # disable https://github.com/ultralytics/yolov5/issues/8493 + + def _cv2_rotate(self, im): + """Rotates a cv2 image based on its orientation; supports 0, 90, and 180 degrees rotations.""" + if self.orientation == 0: + return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE) + elif self.orientation == 180: + return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE) + elif self.orientation == 90: + return cv2.rotate(im, cv2.ROTATE_180) + return im + + def __len__(self): + """Returns the number of files in the dataset.""" + return self.nf # number of files + + +class LoadStreams: + # YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams` + def __init__(self, sources="file.streams", img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): + """Initializes a stream loader for processing video streams with YOLOv5, supporting various sources including + YouTube. + """ + torch.backends.cudnn.benchmark = True # faster for fixed-size inference + self.mode = "stream" + self.img_size = img_size + self.stride = stride + self.vid_stride = vid_stride # video frame-rate stride + sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources] + n = len(sources) + self.sources = [clean_str(x) for x in sources] # clean source names for later + self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n + for i, s in enumerate(sources): # index, source + # Start thread to read frames from video stream + st = f"{i + 1}/{n}: {s}... " + if urlparse(s).hostname in ("www.youtube.com", "youtube.com", "youtu.be"): # if source is YouTube video + # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/LNwODJXcvt4' + check_requirements(("pafy", "youtube_dl==2020.12.2")) + import pafy + + s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL + s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam + if s == 0: + assert not is_colab(), "--source 0 webcam unsupported on Colab. Rerun command in a local environment." + assert not is_kaggle(), "--source 0 webcam unsupported on Kaggle. Rerun command in a local environment." + cap = cv2.VideoCapture(s) + assert cap.isOpened(), f"{st}Failed to open {s}" + w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + fps = cap.get(cv2.CAP_PROP_FPS) # warning: may return 0 or nan + self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float("inf") # infinite stream fallback + self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback + + _, self.imgs[i] = cap.read() # guarantee first frame + self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True) + LOGGER.info(f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)") + self.threads[i].start() + LOGGER.info("") # newline + + # check for common shapes + s = np.stack([letterbox(x, img_size, stride=stride, auto=auto)[0].shape for x in self.imgs]) + self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal + self.auto = auto and self.rect + self.transforms = transforms # optional + if not self.rect: + LOGGER.warning("WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.") + + def update(self, i, cap, stream): + """Reads frames from stream `i`, updating imgs array; handles stream reopening on signal loss.""" + n, f = 0, self.frames[i] # frame number, frame array + while cap.isOpened() and n < f: + n += 1 + cap.grab() # .read() = .grab() followed by .retrieve() + if n % self.vid_stride == 0: + success, im = cap.retrieve() + if success: + self.imgs[i] = im + else: + LOGGER.warning("WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.") + self.imgs[i] = np.zeros_like(self.imgs[i]) + cap.open(stream) # re-open stream if signal was lost + time.sleep(0.0) # wait time + + def __iter__(self): + """Resets and returns the iterator for iterating over video frames or images in a dataset.""" + self.count = -1 + return self + + def __next__(self): + """Iterates over video frames or images, halting on thread stop or 'q' key press, raising `StopIteration` when + done. + """ + self.count += 1 + if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord("q"): # q to quit + cv2.destroyAllWindows() + raise StopIteration + + im0 = self.imgs.copy() + if self.transforms: + im = np.stack([self.transforms(x) for x in im0]) # transforms + else: + im = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0] for x in im0]) # resize + im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW + im = np.ascontiguousarray(im) # contiguous + + return self.sources, im, im0, None, "" + + def __len__(self): + """Returns the number of sources in the dataset, supporting up to 32 streams at 30 FPS over 30 years.""" + return len(self.sources) # 1E12 frames = 32 streams at 30 FPS for 30 years + + +def img2label_paths(img_paths): + """Generates label file paths from corresponding image file paths by replacing `/images/` with `/labels/` and + extension with `.txt`. + """ + sa, sb = f"{os.sep}images{os.sep}", f"{os.sep}labels{os.sep}" # /images/, /labels/ substrings + return [sb.join(x.rsplit(sa, 1)).rsplit(".", 1)[0] + ".txt" for x in img_paths] + + +class LoadImagesAndLabels(Dataset): + # YOLOv5 train_loader/val_loader, loads images and labels for training and validation + cache_version = 0.6 # dataset labels *.cache version + rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4] + + def __init__( + self, + path, + img_size=640, + batch_size=16, + augment=False, + hyp=None, + rect=False, + image_weights=False, + cache_images=False, + single_cls=False, + stride=32, + pad=0.0, + min_items=0, + prefix="", + rank=-1, + seed=0, + ): + """Initializes the YOLOv5 dataset loader, handling images and their labels, caching, and preprocessing.""" + self.img_size = img_size + self.augment = augment + self.hyp = hyp + self.image_weights = image_weights + self.rect = False if image_weights else rect + self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) + self.mosaic_border = [-img_size // 2, -img_size // 2] + self.stride = stride + self.path = path + self.albumentations = Albumentations(size=img_size) if augment else None + + try: + f = [] # image files + for p in path if isinstance(path, list) else [path]: + p = Path(p) # os-agnostic + if p.is_dir(): # dir + f += glob.glob(str(p / "**" / "*.*"), recursive=True) + # f = list(p.rglob('*.*')) # pathlib + elif p.is_file(): # file + with open(p) as t: + t = t.read().strip().splitlines() + parent = str(p.parent) + os.sep + f += [x.replace("./", parent, 1) if x.startswith("./") else x for x in t] # to global path + # f += [p.parent / x.lstrip(os.sep) for x in t] # to global path (pathlib) + else: + raise FileNotFoundError(f"{prefix}{p} does not exist") + self.im_files = sorted(x.replace("/", os.sep) for x in f if x.split(".")[-1].lower() in IMG_FORMATS) + # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS]) # pathlib + assert self.im_files, f"{prefix}No images found" + except Exception as e: + raise Exception(f"{prefix}Error loading data from {path}: {e}\n{HELP_URL}") from e + + # Check cache + self.label_files = img2label_paths(self.im_files) # labels + cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix(".cache") + try: + cache, exists = np.load(cache_path, allow_pickle=True).item(), True # load dict + assert cache["version"] == self.cache_version # matches current version + assert cache["hash"] == get_hash(self.label_files + self.im_files) # identical hash + except Exception: + cache, exists = self.cache_labels(cache_path, prefix), False # run cache ops + + # Display cache + nf, nm, ne, nc, n = cache.pop("results") # found, missing, empty, corrupt, total + if exists and LOCAL_RANK in {-1, 0}: + d = f"Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt" + tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT) # display cache results + if cache["msgs"]: + LOGGER.info("\n".join(cache["msgs"])) # display warnings + assert nf > 0 or not augment, f"{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}" + + # Read cache + [cache.pop(k) for k in ("hash", "version", "msgs")] # remove items + labels, shapes, self.segments = zip(*cache.values()) + nl = len(np.concatenate(labels, 0)) # number of labels + assert nl > 0 or not augment, f"{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}" + self.labels = list(labels) + self.shapes = np.array(shapes) + self.im_files = list(cache.keys()) # update + self.label_files = img2label_paths(cache.keys()) # update + + # Filter images + if min_items: + include = np.array([len(x) >= min_items for x in self.labels]).nonzero()[0].astype(int) + LOGGER.info(f"{prefix}{n - len(include)}/{n} images filtered from dataset") + self.im_files = [self.im_files[i] for i in include] + self.label_files = [self.label_files[i] for i in include] + self.labels = [self.labels[i] for i in include] + self.segments = [self.segments[i] for i in include] + self.shapes = self.shapes[include] # wh + + # Create indices + n = len(self.shapes) # number of images + bi = np.floor(np.arange(n) / batch_size).astype(int) # batch index + nb = bi[-1] + 1 # number of batches + self.batch = bi # batch index of image + self.n = n + self.indices = np.arange(n) + if rank > -1: # DDP indices (see: SmartDistributedSampler) + # force each rank (i.e. GPU process) to sample the same subset of data on every epoch + self.indices = self.indices[np.random.RandomState(seed=seed).permutation(n) % WORLD_SIZE == RANK] + + # Update labels + include_class = [] # filter labels to include only these classes (optional) + self.segments = list(self.segments) + include_class_array = np.array(include_class).reshape(1, -1) + for i, (label, segment) in enumerate(zip(self.labels, self.segments)): + if include_class: + j = (label[:, 0:1] == include_class_array).any(1) + self.labels[i] = label[j] + if segment: + self.segments[i] = [segment[idx] for idx, elem in enumerate(j) if elem] + if single_cls: # single-class training, merge all classes into 0 + self.labels[i][:, 0] = 0 + + # Rectangular Training + if self.rect: + # Sort by aspect ratio + s = self.shapes # wh + ar = s[:, 1] / s[:, 0] # aspect ratio + irect = ar.argsort() + self.im_files = [self.im_files[i] for i in irect] + self.label_files = [self.label_files[i] for i in irect] + self.labels = [self.labels[i] for i in irect] + self.segments = [self.segments[i] for i in irect] + self.shapes = s[irect] # wh + ar = ar[irect] + + # Set training image shapes + shapes = [[1, 1]] * nb + for i in range(nb): + ari = ar[bi == i] + mini, maxi = ari.min(), ari.max() + if maxi < 1: + shapes[i] = [maxi, 1] + elif mini > 1: + shapes[i] = [1, 1 / mini] + + self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride + + # Cache images into RAM/disk for faster training + if cache_images == "ram" and not self.check_cache_ram(prefix=prefix): + cache_images = False + self.ims = [None] * n + self.npy_files = [Path(f).with_suffix(".npy") for f in self.im_files] + if cache_images: + b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes + self.im_hw0, self.im_hw = [None] * n, [None] * n + fcn = self.cache_images_to_disk if cache_images == "disk" else self.load_image + results = ThreadPool(NUM_THREADS).imap(lambda i: (i, fcn(i)), self.indices) + pbar = tqdm(results, total=len(self.indices), bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0) + for i, x in pbar: + if cache_images == "disk": + b += self.npy_files[i].stat().st_size + else: # 'ram' + self.ims[i], self.im_hw0[i], self.im_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i) + b += self.ims[i].nbytes * WORLD_SIZE + pbar.desc = f"{prefix}Caching images ({b / gb:.1f}GB {cache_images})" + pbar.close() + + def check_cache_ram(self, safety_margin=0.1, prefix=""): + """Checks if available RAM is sufficient for caching images, adjusting for a safety margin.""" + b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes + n = min(self.n, 30) # extrapolate from 30 random images + for _ in range(n): + im = cv2.imread(random.choice(self.im_files)) # sample image + ratio = self.img_size / max(im.shape[0], im.shape[1]) # max(h, w) # ratio + b += im.nbytes * ratio**2 + mem_required = b * self.n / n # GB required to cache dataset into RAM + mem = psutil.virtual_memory() + cache = mem_required * (1 + safety_margin) < mem.available # to cache or not to cache, that is the question + if not cache: + LOGGER.info( + f'{prefix}{mem_required / gb:.1f}GB RAM required, ' + f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, ' + f"{'caching images ✅' if cache else 'not caching images ⚠️'}" + ) + return cache + + def cache_labels(self, path=Path("./labels.cache"), prefix=""): + """Caches dataset labels, verifies images, reads shapes, and tracks dataset integrity.""" + x = {} # dict + nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages + desc = f"{prefix}Scanning {path.parent / path.stem}..." + with Pool(NUM_THREADS) as pool: + pbar = tqdm( + pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))), + desc=desc, + total=len(self.im_files), + bar_format=TQDM_BAR_FORMAT, + ) + for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar: + nm += nm_f + nf += nf_f + ne += ne_f + nc += nc_f + if im_file: + x[im_file] = [lb, shape, segments] + if msg: + msgs.append(msg) + pbar.desc = f"{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt" + + pbar.close() + if msgs: + LOGGER.info("\n".join(msgs)) + if nf == 0: + LOGGER.warning(f"{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}") + x["hash"] = get_hash(self.label_files + self.im_files) + x["results"] = nf, nm, ne, nc, len(self.im_files) + x["msgs"] = msgs # warnings + x["version"] = self.cache_version # cache version + try: + np.save(path, x) # save cache for next time + path.with_suffix(".cache.npy").rename(path) # remove .npy suffix + LOGGER.info(f"{prefix}New cache created: {path}") + except Exception as e: + LOGGER.warning(f"{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}") # not writeable + return x + + def __len__(self): + """Returns the number of images in the dataset.""" + return len(self.im_files) + + # def __iter__(self): + # self.count = -1 + # print('ran dataset iter') + # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF) + # return self + + def __getitem__(self, index): + """Fetches the dataset item at the given index, considering linear, shuffled, or weighted sampling.""" + index = self.indices[index] # linear, shuffled, or image_weights + + hyp = self.hyp + mosaic = self.mosaic and random.random() < hyp["mosaic"] + if mosaic: + # Load mosaic + img, labels = self.load_mosaic(index) + shapes = None + + # MixUp augmentation + if random.random() < hyp["mixup"]: + img, labels = mixup(img, labels, *self.load_mosaic(random.choice(self.indices))) + + else: + # Load image + img, (h0, w0), (h, w) = self.load_image(index) + + # Letterbox + shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape + img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) + shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling + + labels = self.labels[index].copy() + if labels.size: # normalized xywh to pixel xyxy format + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) + + if self.augment: + img, labels = random_perspective( + img, + labels, + degrees=hyp["degrees"], + translate=hyp["translate"], + scale=hyp["scale"], + shear=hyp["shear"], + perspective=hyp["perspective"], + ) + + nl = len(labels) # number of labels + if nl: + labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3) + + if self.augment: + # Albumentations + img, labels = self.albumentations(img, labels) + nl = len(labels) # update after albumentations + + # HSV color-space + augment_hsv(img, hgain=hyp["hsv_h"], sgain=hyp["hsv_s"], vgain=hyp["hsv_v"]) + + # Flip up-down + if random.random() < hyp["flipud"]: + img = np.flipud(img) + if nl: + labels[:, 2] = 1 - labels[:, 2] + + # Flip left-right + if random.random() < hyp["fliplr"]: + img = np.fliplr(img) + if nl: + labels[:, 1] = 1 - labels[:, 1] + + # Cutouts + # labels = cutout(img, labels, p=0.5) + # nl = len(labels) # update after cutout + + labels_out = torch.zeros((nl, 6)) + if nl: + labels_out[:, 1:] = torch.from_numpy(labels) + + # Convert + img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB + img = np.ascontiguousarray(img) + + return torch.from_numpy(img), labels_out, self.im_files[index], shapes + + def load_image(self, i): + """ + Loads an image by index, returning the image, its original dimensions, and resized dimensions. + + Returns (im, original hw, resized hw) + """ + im, f, fn = ( + self.ims[i], + self.im_files[i], + self.npy_files[i], + ) + if im is None: # not cached in RAM + if fn.exists(): # load npy + im = np.load(fn) + else: # read image + im = cv2.imread(f) # BGR + assert im is not None, f"Image Not Found {f}" + h0, w0 = im.shape[:2] # orig hw + r = self.img_size / max(h0, w0) # ratio + if r != 1: # if sizes are not equal + interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA + im = cv2.resize(im, (math.ceil(w0 * r), math.ceil(h0 * r)), interpolation=interp) + return im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized + return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized + + def cache_images_to_disk(self, i): + """Saves an image to disk as an *.npy file for quicker loading, identified by index `i`.""" + f = self.npy_files[i] + if not f.exists(): + np.save(f.as_posix(), cv2.imread(self.im_files[i])) + + def load_mosaic(self, index): + """Loads a 4-image mosaic for YOLOv5, combining 1 selected and 3 random images, with labels and segments.""" + labels4, segments4 = [], [] + s = self.img_size + yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y + indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices + random.shuffle(indices) + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = self.load_image(index) + + # place img in img4 + if i == 0: # top left + img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) + x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) + elif i == 1: # top right + x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc + x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h + elif i == 2: # bottom left + x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) + x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) + elif i == 3: # bottom right + x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) + x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) + + img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + padw = x1a - x1b + padh = y1a - y1b + + # Labels + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padw, padh) for x in segments] + labels4.append(labels) + segments4.extend(segments) + + # Concat/clip labels + labels4 = np.concatenate(labels4, 0) + for x in (labels4[:, 1:], *segments4): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img4, labels4 = replicate(img4, labels4) # replicate + + # Augment + img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp["copy_paste"]) + img4, labels4 = random_perspective( + img4, + labels4, + segments4, + degrees=self.hyp["degrees"], + translate=self.hyp["translate"], + scale=self.hyp["scale"], + shear=self.hyp["shear"], + perspective=self.hyp["perspective"], + border=self.mosaic_border, + ) # border to remove + + return img4, labels4 + + def load_mosaic9(self, index): + """Loads 1 image + 8 random images into a 9-image mosaic for augmented YOLOv5 training, returning labels and + segments. + """ + labels9, segments9 = [], [] + s = self.img_size + indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices + random.shuffle(indices) + hp, wp = -1, -1 # height, width previous + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = self.load_image(index) + + # place img in img9 + if i == 0: # center + img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + h0, w0 = h, w + c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates + elif i == 1: # top + c = s, s - h, s + w, s + elif i == 2: # top right + c = s + wp, s - h, s + wp + w, s + elif i == 3: # right + c = s + w0, s, s + w0 + w, s + h + elif i == 4: # bottom right + c = s + w0, s + hp, s + w0 + w, s + hp + h + elif i == 5: # bottom + c = s + w0 - w, s + h0, s + w0, s + h0 + h + elif i == 6: # bottom left + c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h + elif i == 7: # left + c = s - w, s + h0 - h, s, s + h0 + elif i == 8: # top left + c = s - w, s + h0 - hp - h, s, s + h0 - hp + + padx, pady = c[:2] + x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coords + + # Labels + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padx, pady) for x in segments] + labels9.append(labels) + segments9.extend(segments) + + # Image + img9[y1:y2, x1:x2] = img[y1 - pady :, x1 - padx :] # img9[ymin:ymax, xmin:xmax] + hp, wp = h, w # height, width previous + + # Offset + yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border) # mosaic center x, y + img9 = img9[yc : yc + 2 * s, xc : xc + 2 * s] + + # Concat/clip labels + labels9 = np.concatenate(labels9, 0) + labels9[:, [1, 3]] -= xc + labels9[:, [2, 4]] -= yc + c = np.array([xc, yc]) # centers + segments9 = [x - c for x in segments9] + + for x in (labels9[:, 1:], *segments9): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img9, labels9 = replicate(img9, labels9) # replicate + + # Augment + img9, labels9, segments9 = copy_paste(img9, labels9, segments9, p=self.hyp["copy_paste"]) + img9, labels9 = random_perspective( + img9, + labels9, + segments9, + degrees=self.hyp["degrees"], + translate=self.hyp["translate"], + scale=self.hyp["scale"], + shear=self.hyp["shear"], + perspective=self.hyp["perspective"], + border=self.mosaic_border, + ) # border to remove + + return img9, labels9 + + @staticmethod + def collate_fn(batch): + """Batches images, labels, paths, and shapes, assigning unique indices to targets in merged label tensor.""" + im, label, path, shapes = zip(*batch) # transposed + for i, lb in enumerate(label): + lb[:, 0] = i # add target image index for build_targets() + return torch.stack(im, 0), torch.cat(label, 0), path, shapes + + @staticmethod + def collate_fn4(batch): + """Bundles a batch's data by quartering the number of shapes and paths, preparing it for model input.""" + im, label, path, shapes = zip(*batch) # transposed + n = len(shapes) // 4 + im4, label4, path4, shapes4 = [], [], path[:n], shapes[:n] + + ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]]) + wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]]) + s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]]) # scale + for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW + i *= 4 + if random.random() < 0.5: + im1 = F.interpolate(im[i].unsqueeze(0).float(), scale_factor=2.0, mode="bilinear", align_corners=False)[ + 0 + ].type(im[i].type()) + lb = label[i] + else: + im1 = torch.cat((torch.cat((im[i], im[i + 1]), 1), torch.cat((im[i + 2], im[i + 3]), 1)), 2) + lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s + im4.append(im1) + label4.append(lb) + + for i, lb in enumerate(label4): + lb[:, 0] = i # add target image index for build_targets() + + return torch.stack(im4, 0), torch.cat(label4, 0), path4, shapes4 + + +# Ancillary functions -------------------------------------------------------------------------------------------------- +def flatten_recursive(path=DATASETS_DIR / "coco128"): + """Flattens a directory by copying all files from subdirectories to a new top-level directory, preserving + filenames. + """ + new_path = Path(f"{str(path)}_flat") + if os.path.exists(new_path): + shutil.rmtree(new_path) # delete output folder + os.makedirs(new_path) # make new output folder + for file in tqdm(glob.glob(f"{str(Path(path))}/**/*.*", recursive=True)): + shutil.copyfile(file, new_path / Path(file).name) + + +def extract_boxes(path=DATASETS_DIR / "coco128"): + """ + Converts a detection dataset to a classification dataset, creating a directory for each class and extracting + bounding boxes. + + Example: from utils.dataloaders import *; extract_boxes() + """ + path = Path(path) # images dir + shutil.rmtree(path / "classification") if (path / "classification").is_dir() else None # remove existing + files = list(path.rglob("*.*")) + n = len(files) # number of files + for im_file in tqdm(files, total=n): + if im_file.suffix[1:] in IMG_FORMATS: + # image + im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB + h, w = im.shape[:2] + + # labels + lb_file = Path(img2label_paths([str(im_file)])[0]) + if Path(lb_file).exists(): + with open(lb_file) as f: + lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels + + for j, x in enumerate(lb): + c = int(x[0]) # class + f = (path / "classification") / f"{c}" / f"{path.stem}_{im_file.stem}_{j}.jpg" # new filename + if not f.parent.is_dir(): + f.parent.mkdir(parents=True) + + b = x[1:] * [w, h, w, h] # box + # b[2:] = b[2:].max() # rectangle to square + b[2:] = b[2:] * 1.2 + 3 # pad + b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int) + + b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image + b[[1, 3]] = np.clip(b[[1, 3]], 0, h) + assert cv2.imwrite(str(f), im[b[1] : b[3], b[0] : b[2]]), f"box failure in {f}" + + +def autosplit(path=DATASETS_DIR / "coco128/images", weights=(0.9, 0.1, 0.0), annotated_only=False): + """Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files + Usage: from utils.dataloaders import *; autosplit() + Arguments + path: Path to images directory + weights: Train, val, test weights (list, tuple) + annotated_only: Only use images with an annotated txt file + """ + path = Path(path) # images dir + files = sorted(x for x in path.rglob("*.*") if x.suffix[1:].lower() in IMG_FORMATS) # image files only + n = len(files) # number of files + random.seed(0) # for reproducibility + indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split + + txt = ["autosplit_train.txt", "autosplit_val.txt", "autosplit_test.txt"] # 3 txt files + for x in txt: + if (path.parent / x).exists(): + (path.parent / x).unlink() # remove existing + + print(f"Autosplitting images from {path}" + ", using *.txt labeled images only" * annotated_only) + for i, img in tqdm(zip(indices, files), total=n): + if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label + with open(path.parent / txt[i], "a") as f: + f.write(f"./{img.relative_to(path.parent).as_posix()}" + "\n") # add image to txt file + + +def verify_image_label(args): + """Verifies a single image-label pair, ensuring image format, size, and legal label values.""" + im_file, lb_file, prefix = args + nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, "", [] # number (missing, found, empty, corrupt), message, segments + try: + # verify images + im = Image.open(im_file) + im.verify() # PIL verify + shape = exif_size(im) # image size + assert (shape[0] > 9) & (shape[1] > 9), f"image size {shape} <10 pixels" + assert im.format.lower() in IMG_FORMATS, f"invalid image format {im.format}" + if im.format.lower() in ("jpg", "jpeg"): + with open(im_file, "rb") as f: + f.seek(-2, 2) + if f.read() != b"\xff\xd9": # corrupt JPEG + ImageOps.exif_transpose(Image.open(im_file)).save(im_file, "JPEG", subsampling=0, quality=100) + msg = f"{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved" + + # verify labels + if os.path.isfile(lb_file): + nf = 1 # label found + with open(lb_file) as f: + lb = [x.split() for x in f.read().strip().splitlines() if len(x)] + if any(len(x) > 6 for x in lb): # is segment + classes = np.array([x[0] for x in lb], dtype=np.float32) + segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb] # (cls, xy1...) + lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh) + lb = np.array(lb, dtype=np.float32) + nl = len(lb) + if nl: + assert lb.shape[1] == 5, f"labels require 5 columns, {lb.shape[1]} columns detected" + assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}" + assert (lb[:, 1:] <= 1).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}" + _, i = np.unique(lb, axis=0, return_index=True) + if len(i) < nl: # duplicate row check + lb = lb[i] # remove duplicates + if segments: + segments = [segments[x] for x in i] + msg = f"{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed" + else: + ne = 1 # label empty + lb = np.zeros((0, 5), dtype=np.float32) + else: + nm = 1 # label missing + lb = np.zeros((0, 5), dtype=np.float32) + return im_file, lb, shape, segments, nm, nf, ne, nc, msg + except Exception as e: + nc = 1 + msg = f"{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}" + return [None, None, None, None, nm, nf, ne, nc, msg] + + +class HUBDatasetStats: + """ + Class for generating HUB dataset JSON and `-hub` dataset directory. + + Arguments + path: Path to data.yaml or data.zip (with data.yaml inside data.zip) + autodownload: Attempt to download dataset if not found locally + + Usage + from utils.dataloaders import HUBDatasetStats + stats = HUBDatasetStats('coco128.yaml', autodownload=True) # usage 1 + stats = HUBDatasetStats('path/to/coco128.zip') # usage 2 + stats.get_json(save=False) + stats.process_images() + """ + + def __init__(self, path="coco128.yaml", autodownload=False): + """Initializes HUBDatasetStats with optional auto-download for datasets, given a path to dataset YAML or ZIP + file. + """ + zipped, data_dir, yaml_path = self._unzip(Path(path)) + try: + with open(check_yaml(yaml_path), errors="ignore") as f: + data = yaml.safe_load(f) # data dict + if zipped: + data["path"] = data_dir + except Exception as e: + raise Exception("error/HUB/dataset_stats/yaml_load") from e + + check_dataset(data, autodownload) # download dataset if missing + self.hub_dir = Path(data["path"] + "-hub") + self.im_dir = self.hub_dir / "images" + self.im_dir.mkdir(parents=True, exist_ok=True) # makes /images + self.stats = {"nc": data["nc"], "names": list(data["names"].values())} # statistics dictionary + self.data = data + + @staticmethod + def _find_yaml(dir): + """Finds and returns the path to a single '.yaml' file in the specified directory, preferring files that match + the directory name. + """ + files = list(dir.glob("*.yaml")) or list(dir.rglob("*.yaml")) # try root level first and then recursive + assert files, f"No *.yaml file found in {dir}" + if len(files) > 1: + files = [f for f in files if f.stem == dir.stem] # prefer *.yaml files that match dir name + assert files, f"Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed" + assert len(files) == 1, f"Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}" + return files[0] + + def _unzip(self, path): + """Unzips a .zip file at 'path', returning success status, unzipped directory, and path to YAML file within.""" + if not str(path).endswith(".zip"): # path is data.yaml + return False, None, path + assert Path(path).is_file(), f"Error unzipping {path}, file not found" + unzip_file(path, path=path.parent) + dir = path.with_suffix("") # dataset directory == zip name + assert dir.is_dir(), f"Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/" + return True, str(dir), self._find_yaml(dir) # zipped, data_dir, yaml_path + + def _hub_ops(self, f, max_dim=1920): + """Resizes and saves an image at reduced quality for web/app viewing, supporting both PIL and OpenCV.""" + f_new = self.im_dir / Path(f).name # dataset-hub image filename + try: # use PIL + im = Image.open(f) + r = max_dim / max(im.height, im.width) # ratio + if r < 1.0: # image too large + im = im.resize((int(im.width * r), int(im.height * r))) + im.save(f_new, "JPEG", quality=50, optimize=True) # save + except Exception as e: # use OpenCV + LOGGER.info(f"WARNING ⚠️ HUB ops PIL failure {f}: {e}") + im = cv2.imread(f) + im_height, im_width = im.shape[:2] + r = max_dim / max(im_height, im_width) # ratio + if r < 1.0: # image too large + im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA) + cv2.imwrite(str(f_new), im) + + def get_json(self, save=False, verbose=False): + """Generates dataset JSON for Ultralytics HUB, optionally saves or prints it; save=bool, verbose=bool.""" + + def _round(labels): + """Rounds class labels to integers and coordinates to 4 decimal places for improved label accuracy.""" + return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels] + + for split in "train", "val", "test": + if self.data.get(split) is None: + self.stats[split] = None # i.e. no test set + continue + dataset = LoadImagesAndLabels(self.data[split]) # load dataset + x = np.array( + [ + np.bincount(label[:, 0].astype(int), minlength=self.data["nc"]) + for label in tqdm(dataset.labels, total=dataset.n, desc="Statistics") + ] + ) # shape(128x80) + self.stats[split] = { + "instance_stats": {"total": int(x.sum()), "per_class": x.sum(0).tolist()}, + "image_stats": { + "total": dataset.n, + "unlabelled": int(np.all(x == 0, 1).sum()), + "per_class": (x > 0).sum(0).tolist(), + }, + "labels": [{str(Path(k).name): _round(v.tolist())} for k, v in zip(dataset.im_files, dataset.labels)], + } + + # Save, print and return + if save: + stats_path = self.hub_dir / "stats.json" + print(f"Saving {stats_path.resolve()}...") + with open(stats_path, "w") as f: + json.dump(self.stats, f) # save stats.json + if verbose: + print(json.dumps(self.stats, indent=2, sort_keys=False)) + return self.stats + + def process_images(self): + """Compresses images for Ultralytics HUB across 'train', 'val', 'test' splits and saves to specified + directory. + """ + for split in "train", "val", "test": + if self.data.get(split) is None: + continue + dataset = LoadImagesAndLabels(self.data[split]) # load dataset + desc = f"{split} images" + for _ in tqdm(ThreadPool(NUM_THREADS).imap(self._hub_ops, dataset.im_files), total=dataset.n, desc=desc): + pass + print(f"Done. All images saved to {self.im_dir}") + return self.im_dir + + +# Classification dataloaders ------------------------------------------------------------------------------------------- +class ClassificationDataset(torchvision.datasets.ImageFolder): + """ + YOLOv5 Classification Dataset. + + Arguments + root: Dataset path + transform: torchvision transforms, used by default + album_transform: Albumentations transforms, used if installed + """ + + def __init__(self, root, augment, imgsz, cache=False): + """Initializes YOLOv5 Classification Dataset with optional caching, augmentations, and transforms for image + classification. + """ + super().__init__(root=root) + self.torch_transforms = classify_transforms(imgsz) + self.album_transforms = classify_albumentations(augment, imgsz) if augment else None + self.cache_ram = cache is True or cache == "ram" + self.cache_disk = cache == "disk" + self.samples = [list(x) + [Path(x[0]).with_suffix(".npy"), None] for x in self.samples] # file, index, npy, im + + def __getitem__(self, i): + """Fetches and transforms an image sample by index, supporting RAM/disk caching and Augmentations.""" + f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image + if self.cache_ram and im is None: + im = self.samples[i][3] = cv2.imread(f) + elif self.cache_disk: + if not fn.exists(): # load npy + np.save(fn.as_posix(), cv2.imread(f)) + im = np.load(fn) + else: # read image + im = cv2.imread(f) # BGR + if self.album_transforms: + sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))["image"] + else: + sample = self.torch_transforms(im) + return sample, j + + +def create_classification_dataloader( + path, imgsz=224, batch_size=16, augment=True, cache=False, rank=-1, workers=8, shuffle=True +): + # Returns Dataloader object to be used with YOLOv5 Classifier + """Creates a DataLoader for image classification, supporting caching, augmentation, and distributed training.""" + with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP + dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache) + batch_size = min(batch_size, len(dataset)) + nd = torch.cuda.device_count() + nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) + sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) + generator = torch.Generator() + generator.manual_seed(6148914691236517205 + RANK) + return InfiniteDataLoader( + dataset, + batch_size=batch_size, + shuffle=shuffle and sampler is None, + num_workers=nw, + sampler=sampler, + pin_memory=PIN_MEMORY, + worker_init_fn=seed_worker, + generator=generator, + ) # or DataLoader(persistent_workers=True) diff --git a/yolov5/utils/docker/Dockerfile b/yolov5/utils/docker/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..f4727162065a1b1b1fd2b54cc1eac4bfb4a62289 --- /dev/null +++ b/yolov5/utils/docker/Dockerfile @@ -0,0 +1,73 @@ +# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license +# Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 +# Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference + +# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch +FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime + +# Downloads to user config dir +ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ + +# Install linux packages +ENV DEBIAN_FRONTEND noninteractive +RUN apt update +RUN TZ=Etc/UTC apt install -y tzdata +RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg +# RUN alias python=python3 + +# Security updates +# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796 +RUN apt upgrade --no-install-recommends -y openssl + +# Create working directory +RUN rm -rf /usr/src/app && mkdir -p /usr/src/app +WORKDIR /usr/src/app + +# Copy contents +COPY . /usr/src/app + +# Install pip packages +COPY requirements.txt . +RUN python3 -m pip install --upgrade pip wheel +RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook \ + coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0' + # tensorflow tensorflowjs \ + +# Set environment variables +ENV OMP_NUM_THREADS=1 + +# Cleanup +ENV DEBIAN_FRONTEND teletype + + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/yolov5:latest && sudo docker build -f utils/docker/Dockerfile -t $t . && sudo docker push $t + +# Pull and Run +# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t + +# Pull and Run with local directory access +# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t + +# Kill all +# sudo docker kill $(sudo docker ps -q) + +# Kill all image-based +# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest) + +# DockerHub tag update +# t=ultralytics/yolov5:latest tnew=ultralytics/yolov5:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew + +# Clean up +# sudo docker system prune -a --volumes + +# Update Ubuntu drivers +# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/ + +# DDP test +# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3 + +# GCP VM from Image +# docker.io/ultralytics/yolov5:latest diff --git a/yolov5/utils/docker/Dockerfile-arm64 b/yolov5/utils/docker/Dockerfile-arm64 new file mode 100644 index 0000000000000000000000000000000000000000..0de85bf8d6098812fea0c3db1e5a14aa65b3a2cd --- /dev/null +++ b/yolov5/utils/docker/Dockerfile-arm64 @@ -0,0 +1,40 @@ +# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license +# Builds ultralytics/yolov5:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 +# Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi + +# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu +FROM arm64v8/ubuntu:22.10 + +# Downloads to user config dir +ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ + +# Install linux packages +ENV DEBIAN_FRONTEND noninteractive +RUN apt update +RUN TZ=Etc/UTC apt install -y tzdata +RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1 libglib2.0-0 libpython3-dev +# RUN alias python=python3 + +# Install pip packages +COPY requirements.txt . +RUN python3 -m pip install --upgrade pip wheel +RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \ + coremltools onnx onnxruntime + # tensorflow-aarch64 tensorflowjs \ + +# Create working directory +RUN mkdir -p /usr/src/app +WORKDIR /usr/src/app + +# Copy contents +COPY . /usr/src/app +ENV DEBIAN_FRONTEND teletype + + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/yolov5:latest-arm64 && sudo docker build --platform linux/arm64 -f utils/docker/Dockerfile-arm64 -t $t . && sudo docker push $t + +# Pull and Run +# t=ultralytics/yolov5:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t diff --git a/yolov5/utils/docker/Dockerfile-cpu b/yolov5/utils/docker/Dockerfile-cpu new file mode 100644 index 0000000000000000000000000000000000000000..573ad3276e731ba08ef84ef747a7ae326367c94d --- /dev/null +++ b/yolov5/utils/docker/Dockerfile-cpu @@ -0,0 +1,42 @@ +# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license +# Builds ultralytics/yolov5:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 +# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments + +# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu +FROM ubuntu:23.10 + +# Downloads to user config dir +ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ + +# Install linux packages +# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package +RUN apt update \ + && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 +# RUN alias python=python3 + +# Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error +RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED + +# Install pip packages +COPY requirements.txt . +RUN python3 -m pip install --upgrade pip wheel +RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \ + coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0' \ + # tensorflow tensorflowjs \ + --extra-index-url https://download.pytorch.org/whl/cpu + +# Create working directory +RUN mkdir -p /usr/src/app +WORKDIR /usr/src/app + +# Copy contents +COPY . /usr/src/app + + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/yolov5:latest-cpu && sudo docker build -f utils/docker/Dockerfile-cpu -t $t . && sudo docker push $t + +# Pull and Run +# t=ultralytics/yolov5:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t diff --git a/yolov5/utils/downloads.py b/yolov5/utils/downloads.py new file mode 100644 index 0000000000000000000000000000000000000000..c7e2273c794e5288315d752669237145ae5f5cf3 --- /dev/null +++ b/yolov5/utils/downloads.py @@ -0,0 +1,136 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Download utils.""" + +import logging +import subprocess +import urllib +from pathlib import Path + +import requests +import torch + + +def is_url(url, check=True): + """Determines if a string is a URL and optionally checks its existence online, returning a boolean.""" + try: + url = str(url) + result = urllib.parse.urlparse(url) + assert all([result.scheme, result.netloc]) # check if is url + return (urllib.request.urlopen(url).getcode() == 200) if check else True # check if exists online + except (AssertionError, urllib.request.HTTPError): + return False + + +def gsutil_getsize(url=""): + """ + Returns the size in bytes of a file at a Google Cloud Storage URL using `gsutil du`. + + Returns 0 if the command fails or output is empty. + """ + output = subprocess.check_output(["gsutil", "du", url], shell=True, encoding="utf-8") + return int(output.split()[0]) if output else 0 + + +def url_getsize(url="https://ultralytics.com/images/bus.jpg"): + """Returns the size in bytes of a downloadable file at a given URL; defaults to -1 if not found.""" + response = requests.head(url, allow_redirects=True) + return int(response.headers.get("content-length", -1)) + + +def curl_download(url, filename, *, silent: bool = False) -> bool: + """Download a file from a url to a filename using curl.""" + silent_option = "sS" if silent else "" # silent + proc = subprocess.run( + [ + "curl", + "-#", + f"-{silent_option}L", + url, + "--output", + filename, + "--retry", + "9", + "-C", + "-", + ] + ) + return proc.returncode == 0 + + +def safe_download(file, url, url2=None, min_bytes=1e0, error_msg=""): + """ + Downloads a file from a URL (or alternate URL) to a specified path if file is above a minimum size. + + Removes incomplete downloads. + """ + from utils.general import LOGGER + + file = Path(file) + assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}" + try: # url1 + LOGGER.info(f"Downloading {url} to {file}...") + torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO) + assert file.exists() and file.stat().st_size > min_bytes, assert_msg # check + except Exception as e: # url2 + if file.exists(): + file.unlink() # remove partial downloads + LOGGER.info(f"ERROR: {e}\nRe-attempting {url2 or url} to {file}...") + # curl download, retry and resume on fail + curl_download(url2 or url, file) + finally: + if not file.exists() or file.stat().st_size < min_bytes: # check + if file.exists(): + file.unlink() # remove partial downloads + LOGGER.info(f"ERROR: {assert_msg}\n{error_msg}") + LOGGER.info("") + + +def attempt_download(file, repo="ultralytics/yolov5", release="v7.0"): + """Downloads a file from GitHub release assets or via direct URL if not found locally, supporting backup + versions. + """ + from utils.general import LOGGER + + def github_assets(repository, version="latest"): + """Fetches GitHub repository release tag and asset names using the GitHub API.""" + if version != "latest": + version = f"tags/{version}" # i.e. tags/v7.0 + response = requests.get(f"https://api.github.com/repos/{repository}/releases/{version}").json() # github api + return response["tag_name"], [x["name"] for x in response["assets"]] # tag, assets + + file = Path(str(file).strip().replace("'", "")) + if not file.exists(): + # URL specified + name = Path(urllib.parse.unquote(str(file))).name # decode '%2F' to '/' etc. + if str(file).startswith(("http:/", "https:/")): # download + url = str(file).replace(":/", "://") # Pathlib turns :// -> :/ + file = name.split("?")[0] # parse authentication https://url.com/file.txt?auth... + if Path(file).is_file(): + LOGGER.info(f"Found {url} locally at {file}") # file already exists + else: + safe_download(file=file, url=url, min_bytes=1e5) + return file + + # GitHub assets + assets = [f"yolov5{size}{suffix}.pt" for size in "nsmlx" for suffix in ("", "6", "-cls", "-seg")] # default + try: + tag, assets = github_assets(repo, release) + except Exception: + try: + tag, assets = github_assets(repo) # latest release + except Exception: + try: + tag = subprocess.check_output("git tag", shell=True, stderr=subprocess.STDOUT).decode().split()[-1] + except Exception: + tag = release + + if name in assets: + file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required) + safe_download( + file, + url=f"https://github.com/{repo}/releases/download/{tag}/{name}", + min_bytes=1e5, + error_msg=f"{file} missing, try downloading from https://github.com/{repo}/releases/{tag}", + ) + + return str(file) diff --git a/yolov5/utils/flask_rest_api/README.md b/yolov5/utils/flask_rest_api/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b18a3011cf32d792e5447fca10d3278617732ab4 --- /dev/null +++ b/yolov5/utils/flask_rest_api/README.md @@ -0,0 +1,70 @@ +# Flask REST API + +[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/). + +## Requirements + +[Flask](https://palletsprojects.com/p/flask/) is required. Install with: + +```shell +$ pip install Flask +``` + +## Run + +After Flask installation run: + +```shell +$ python3 restapi.py --port 5000 +``` + +Then use [curl](https://curl.se/) to perform a request: + +```shell +$ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s' +``` + +The model inference results are returned as a JSON response: + +```json +[ + { + "class": 0, + "confidence": 0.8900438547, + "height": 0.9318675399, + "name": "person", + "width": 0.3264600933, + "xcenter": 0.7438579798, + "ycenter": 0.5207948685 + }, + { + "class": 0, + "confidence": 0.8440024257, + "height": 0.7155083418, + "name": "person", + "width": 0.6546785235, + "xcenter": 0.427829951, + "ycenter": 0.6334488392 + }, + { + "class": 27, + "confidence": 0.3771208823, + "height": 0.3902671337, + "name": "tie", + "width": 0.0696444362, + "xcenter": 0.3675483763, + "ycenter": 0.7991207838 + }, + { + "class": 27, + "confidence": 0.3527112305, + "height": 0.1540903747, + "name": "tie", + "width": 0.0336618312, + "xcenter": 0.7814827561, + "ycenter": 0.5065554976 + } +] +``` + +An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given in `example_request.py` diff --git a/yolov5/utils/flask_rest_api/example_request.py b/yolov5/utils/flask_rest_api/example_request.py new file mode 100644 index 0000000000000000000000000000000000000000..104249002aa3cdb0d78f7cc26a541a74b339d4b1 --- /dev/null +++ b/yolov5/utils/flask_rest_api/example_request.py @@ -0,0 +1,17 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Perform test request.""" + +import pprint + +import requests + +DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s" +IMAGE = "zidane.jpg" + +# Read image +with open(IMAGE, "rb") as f: + image_data = f.read() + +response = requests.post(DETECTION_URL, files={"image": image_data}).json() + +pprint.pprint(response) diff --git a/yolov5/utils/flask_rest_api/restapi.py b/yolov5/utils/flask_rest_api/restapi.py new file mode 100644 index 0000000000000000000000000000000000000000..7e03d3a6679a2d6ca57cbc169dc116f08ec30f23 --- /dev/null +++ b/yolov5/utils/flask_rest_api/restapi.py @@ -0,0 +1,49 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Run a Flask REST API exposing one or more YOLOv5s models.""" + +import argparse +import io + +import torch +from flask import Flask, request +from PIL import Image + +app = Flask(__name__) +models = {} + +DETECTION_URL = "/v1/object-detection/" + + +@app.route(DETECTION_URL, methods=["POST"]) +def predict(model): + """Predict and return object detections in JSON format given an image and model name via a Flask REST API POST + request. + """ + if request.method != "POST": + return + + if request.files.get("image"): + # Method 1 + # with request.files["image"] as f: + # im = Image.open(io.BytesIO(f.read())) + + # Method 2 + im_file = request.files["image"] + im_bytes = im_file.read() + im = Image.open(io.BytesIO(im_bytes)) + + if model in models: + results = models[model](im, size=640) # reduce size=320 for faster inference + return results.pandas().xyxy[0].to_json(orient="records") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model") + parser.add_argument("--port", default=5000, type=int, help="port number") + parser.add_argument("--model", nargs="+", default=["yolov5s"], help="model(s) to run, i.e. --model yolov5n yolov5s") + opt = parser.parse_args() + + for m in opt.model: + models[m] = torch.hub.load("ultralytics/yolov5", m, force_reload=True, skip_validation=True) + + app.run(host="0.0.0.0", port=opt.port) # debug=True causes Restarting with stat diff --git a/yolov5/utils/general.py b/yolov5/utils/general.py new file mode 100644 index 0000000000000000000000000000000000000000..95a76644776f56b7d8dd6279ca3748b627b6fae7 --- /dev/null +++ b/yolov5/utils/general.py @@ -0,0 +1,1293 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""General utils.""" + +import contextlib +import glob +import inspect +import logging +import logging.config +import math +import os +import platform +import random +import re +import signal +import subprocess +import sys +import time +import urllib +from copy import deepcopy +from datetime import datetime +from itertools import repeat +from multiprocessing.pool import ThreadPool +from pathlib import Path +from subprocess import check_output +from tarfile import is_tarfile +from typing import Optional +from zipfile import ZipFile, is_zipfile + +import cv2 +import numpy as np +import pandas as pd +import pkg_resources as pkg +import torch +import torchvision +import yaml + +# Import 'ultralytics' package or install if missing +try: + import ultralytics + + assert hasattr(ultralytics, "__version__") # verify package is not directory +except (ImportError, AssertionError): + os.system("pip install -U ultralytics") + import ultralytics + +from ultralytics.utils.checks import check_requirements + +from utils import TryExcept, emojis +from utils.downloads import curl_download, gsutil_getsize +from utils.metrics import box_iou, fitness + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +RANK = int(os.getenv("RANK", -1)) + +# Settings +NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads +DATASETS_DIR = Path(os.getenv("YOLOv5_DATASETS_DIR", ROOT.parent / "datasets")) # global datasets directory +AUTOINSTALL = str(os.getenv("YOLOv5_AUTOINSTALL", True)).lower() == "true" # global auto-install mode +VERBOSE = str(os.getenv("YOLOv5_VERBOSE", True)).lower() == "true" # global verbose mode +TQDM_BAR_FORMAT = "{l_bar}{bar:10}{r_bar}" # tqdm bar format +FONT = "Arial.ttf" # https://ultralytics.com/assets/Arial.ttf + +torch.set_printoptions(linewidth=320, precision=5, profile="long") +np.set_printoptions(linewidth=320, formatter={"float_kind": "{:11.5g}".format}) # format short g, %precision=5 +pd.options.display.max_columns = 10 +cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) +os.environ["NUMEXPR_MAX_THREADS"] = str(NUM_THREADS) # NumExpr max threads +os.environ["OMP_NUM_THREADS"] = "1" if platform.system() == "darwin" else str(NUM_THREADS) # OpenMP (PyTorch and SciPy) +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" # suppress verbose TF compiler warnings in Colab +os.environ["TORCH_CPP_LOG_LEVEL"] = "ERROR" # suppress "NNPACK.cpp could not initialize NNPACK" warnings +os.environ["KINETO_LOG_LEVEL"] = "5" # suppress verbose PyTorch profiler output when computing FLOPs + + +def is_ascii(s=""): + """Checks if input string `s` contains only ASCII characters; returns `True` if so, otherwise `False`.""" + s = str(s) # convert list, tuple, None, etc. to str + return len(s.encode().decode("ascii", "ignore")) == len(s) + + +def is_chinese(s="人工智能"): + """Determines if a string `s` contains any Chinese characters; returns `True` if so, otherwise `False`.""" + return bool(re.search("[\u4e00-\u9fff]", str(s))) + + +def is_colab(): + """Checks if the current environment is a Google Colab instance; returns `True` for Colab, otherwise `False`.""" + return "google.colab" in sys.modules + + +def is_jupyter(): + """ + Check if the current script is running inside a Jupyter Notebook. Verified on Colab, Jupyterlab, Kaggle, Paperspace. + + Returns: + bool: True if running inside a Jupyter Notebook, False otherwise. + """ + with contextlib.suppress(Exception): + from IPython import get_ipython + + return get_ipython() is not None + return False + + +def is_kaggle(): + """Checks if the current environment is a Kaggle Notebook by validating environment variables.""" + return os.environ.get("PWD") == "/kaggle/working" and os.environ.get("KAGGLE_URL_BASE") == "https://www.kaggle.com" + + +def is_docker() -> bool: + """Check if the process runs inside a docker container.""" + if Path("/.dockerenv").exists(): + return True + try: # check if docker is in control groups + with open("/proc/self/cgroup") as file: + return any("docker" in line for line in file) + except OSError: + return False + + +def is_writeable(dir, test=False): + """Checks if a directory is writable, optionally testing by creating a temporary file if `test=True`.""" + if not test: + return os.access(dir, os.W_OK) # possible issues on Windows + file = Path(dir) / "tmp.txt" + try: + with open(file, "w"): # open file with write permissions + pass + file.unlink() # remove file + return True + except OSError: + return False + + +LOGGING_NAME = "yolov5" + + +def set_logging(name=LOGGING_NAME, verbose=True): + """Configures logging with specified verbosity; `name` sets the logger's name, `verbose` controls logging level.""" + rank = int(os.getenv("RANK", -1)) # rank in world for Multi-GPU trainings + level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR + logging.config.dictConfig( + { + "version": 1, + "disable_existing_loggers": False, + "formatters": {name: {"format": "%(message)s"}}, + "handlers": { + name: { + "class": "logging.StreamHandler", + "formatter": name, + "level": level, + } + }, + "loggers": { + name: { + "level": level, + "handlers": [name], + "propagate": False, + } + }, + } + ) + + +set_logging(LOGGING_NAME) # run before defining LOGGER +LOGGER = logging.getLogger(LOGGING_NAME) # define globally (used in train.py, val.py, detect.py, etc.) +if platform.system() == "Windows": + for fn in LOGGER.info, LOGGER.warning: + setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x))) # emoji safe logging + + +def user_config_dir(dir="Ultralytics", env_var="YOLOV5_CONFIG_DIR"): + """Returns user configuration directory path, preferring environment variable `YOLOV5_CONFIG_DIR` if set, else OS- + specific. + """ + env = os.getenv(env_var) + if env: + path = Path(env) # use environment variable + else: + cfg = {"Windows": "AppData/Roaming", "Linux": ".config", "Darwin": "Library/Application Support"} # 3 OS dirs + path = Path.home() / cfg.get(platform.system(), "") # OS-specific config dir + path = (path if is_writeable(path) else Path("/tmp")) / dir # GCP and AWS lambda fix, only /tmp is writeable + path.mkdir(exist_ok=True) # make if required + return path + + +CONFIG_DIR = user_config_dir() # Ultralytics settings dir + + +class Profile(contextlib.ContextDecorator): + # YOLOv5 Profile class. Usage: @Profile() decorator or 'with Profile():' context manager + def __init__(self, t=0.0, device: torch.device = None): + """Initializes a profiling context for YOLOv5 with optional timing threshold and device specification.""" + self.t = t + self.device = device + self.cuda = bool(device and str(device).startswith("cuda")) + + def __enter__(self): + """Initializes timing at the start of a profiling context block for performance measurement.""" + self.start = self.time() + return self + + def __exit__(self, type, value, traceback): + """Concludes timing, updating duration for profiling upon exiting a context block.""" + self.dt = self.time() - self.start # delta-time + self.t += self.dt # accumulate dt + + def time(self): + """Measures and returns the current time, synchronizing CUDA operations if `cuda` is True.""" + if self.cuda: + torch.cuda.synchronize(self.device) + return time.time() + + +class Timeout(contextlib.ContextDecorator): + # YOLOv5 Timeout class. Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager + def __init__(self, seconds, *, timeout_msg="", suppress_timeout_errors=True): + """Initializes a timeout context/decorator with defined seconds, optional message, and error suppression.""" + self.seconds = int(seconds) + self.timeout_message = timeout_msg + self.suppress = bool(suppress_timeout_errors) + + def _timeout_handler(self, signum, frame): + """Raises a TimeoutError with a custom message when a timeout event occurs.""" + raise TimeoutError(self.timeout_message) + + def __enter__(self): + """Initializes timeout mechanism on non-Windows platforms, starting a countdown to raise TimeoutError.""" + if platform.system() != "Windows": # not supported on Windows + signal.signal(signal.SIGALRM, self._timeout_handler) # Set handler for SIGALRM + signal.alarm(self.seconds) # start countdown for SIGALRM to be raised + + def __exit__(self, exc_type, exc_val, exc_tb): + """Disables active alarm on non-Windows systems and optionally suppresses TimeoutError if set.""" + if platform.system() != "Windows": + signal.alarm(0) # Cancel SIGALRM if it's scheduled + if self.suppress and exc_type is TimeoutError: # Suppress TimeoutError + return True + + +class WorkingDirectory(contextlib.ContextDecorator): + # Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager + def __init__(self, new_dir): + """Initializes a context manager/decorator to temporarily change the working directory.""" + self.dir = new_dir # new dir + self.cwd = Path.cwd().resolve() # current dir + + def __enter__(self): + """Temporarily changes the working directory within a 'with' statement context.""" + os.chdir(self.dir) + + def __exit__(self, exc_type, exc_val, exc_tb): + """Restores the original working directory upon exiting a 'with' statement context.""" + os.chdir(self.cwd) + + +def methods(instance): + """Returns list of method names for a class/instance excluding dunder methods.""" + return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")] + + +def print_args(args: Optional[dict] = None, show_file=True, show_func=False): + """Logs the arguments of the calling function, with options to include the filename and function name.""" + x = inspect.currentframe().f_back # previous frame + file, _, func, _, _ = inspect.getframeinfo(x) + if args is None: # get args automatically + args, _, _, frm = inspect.getargvalues(x) + args = {k: v for k, v in frm.items() if k in args} + try: + file = Path(file).resolve().relative_to(ROOT).with_suffix("") + except ValueError: + file = Path(file).stem + s = (f"{file}: " if show_file else "") + (f"{func}: " if show_func else "") + LOGGER.info(colorstr(s) + ", ".join(f"{k}={v}" for k, v in args.items())) + + +def init_seeds(seed=0, deterministic=False): + """ + Initializes RNG seeds and sets deterministic options if specified. + + See https://pytorch.org/docs/stable/notes/randomness.html + """ + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe + # torch.backends.cudnn.benchmark = True # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287 + if deterministic and check_version(torch.__version__, "1.12.0"): # https://github.com/ultralytics/yolov5/pull/8213 + torch.use_deterministic_algorithms(True) + torch.backends.cudnn.deterministic = True + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + os.environ["PYTHONHASHSEED"] = str(seed) + + +def intersect_dicts(da, db, exclude=()): + """Returns intersection of `da` and `db` dicts with matching keys and shapes, excluding `exclude` keys; uses `da` + values. + """ + return {k: v for k, v in da.items() if k in db and all(x not in k for x in exclude) and v.shape == db[k].shape} + + +def get_default_args(func): + """Returns a dict of `func` default arguments by inspecting its signature.""" + signature = inspect.signature(func) + return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty} + + +def get_latest_run(search_dir="."): + """Returns the path to the most recent 'last.pt' file in /runs to resume from, searches in `search_dir`.""" + last_list = glob.glob(f"{search_dir}/**/last*.pt", recursive=True) + return max(last_list, key=os.path.getctime) if last_list else "" + + +def file_age(path=__file__): + """Calculates and returns the age of a file in days based on its last modification time.""" + dt = datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime) # delta + return dt.days # + dt.seconds / 86400 # fractional days + + +def file_date(path=__file__): + """Returns a human-readable file modification date in 'YYYY-M-D' format, given a file path.""" + t = datetime.fromtimestamp(Path(path).stat().st_mtime) + return f"{t.year}-{t.month}-{t.day}" + + +def file_size(path): + """Returns file or directory size in megabytes (MB) for a given path, where directories are recursively summed.""" + mb = 1 << 20 # bytes to MiB (1024 ** 2) + path = Path(path) + if path.is_file(): + return path.stat().st_size / mb + elif path.is_dir(): + return sum(f.stat().st_size for f in path.glob("**/*") if f.is_file()) / mb + else: + return 0.0 + + +def check_online(): + """Checks internet connectivity by attempting to create a connection to "1.1.1.1" on port 443, retries once if the + first attempt fails. + """ + import socket + + def run_once(): + """Checks internet connectivity by attempting to create a connection to "1.1.1.1" on port 443.""" + try: + socket.create_connection(("1.1.1.1", 443), 5) # check host accessibility + return True + except OSError: + return False + + return run_once() or run_once() # check twice to increase robustness to intermittent connectivity issues + + +def git_describe(path=ROOT): + """ + Returns a human-readable git description of the repository at `path`, or an empty string on failure. + + Example output is 'fv5.0-5-g3e25f1e'. See https://git-scm.com/docs/git-describe. + """ + try: + assert (Path(path) / ".git").is_dir() + return check_output(f"git -C {path} describe --tags --long --always", shell=True).decode()[:-1] + except Exception: + return "" + + +@TryExcept() +@WorkingDirectory(ROOT) +def check_git_status(repo="ultralytics/yolov5", branch="master"): + """Checks if YOLOv5 code is up-to-date with the repository, advising 'git pull' if behind; errors return informative + messages. + """ + url = f"https://github.com/{repo}" + msg = f", for updates see {url}" + s = colorstr("github: ") # string + assert Path(".git").exists(), s + "skipping check (not a git repository)" + msg + assert check_online(), s + "skipping check (offline)" + msg + + splits = re.split(pattern=r"\s", string=check_output("git remote -v", shell=True).decode()) + matches = [repo in s for s in splits] + if any(matches): + remote = splits[matches.index(True) - 1] + else: + remote = "ultralytics" + check_output(f"git remote add {remote} {url}", shell=True) + check_output(f"git fetch {remote}", shell=True, timeout=5) # git fetch + local_branch = check_output("git rev-parse --abbrev-ref HEAD", shell=True).decode().strip() # checked out + n = int(check_output(f"git rev-list {local_branch}..{remote}/{branch} --count", shell=True)) # commits behind + if n > 0: + pull = "git pull" if remote == "origin" else f"git pull {remote} {branch}" + s += f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use '{pull}' or 'git clone {url}' to update." + else: + s += f"up to date with {url} ✅" + LOGGER.info(s) + + +@WorkingDirectory(ROOT) +def check_git_info(path="."): + """Checks YOLOv5 git info, returning a dict with remote URL, branch name, and commit hash.""" + check_requirements("gitpython") + import git + + try: + repo = git.Repo(path) + remote = repo.remotes.origin.url.replace(".git", "") # i.e. 'https://github.com/ultralytics/yolov5' + commit = repo.head.commit.hexsha # i.e. '3134699c73af83aac2a481435550b968d5792c0d' + try: + branch = repo.active_branch.name # i.e. 'main' + except TypeError: # not on any branch + branch = None # i.e. 'detached HEAD' state + return {"remote": remote, "branch": branch, "commit": commit} + except git.exc.InvalidGitRepositoryError: # path is not a git dir + return {"remote": None, "branch": None, "commit": None} + + +def check_python(minimum="3.8.0"): + """Checks if current Python version meets the minimum required version, exits if not.""" + check_version(platform.python_version(), minimum, name="Python ", hard=True) + + +def check_version(current="0.0.0", minimum="0.0.0", name="version ", pinned=False, hard=False, verbose=False): + """Checks if the current version meets the minimum required version, exits or warns based on parameters.""" + current, minimum = (pkg.parse_version(x) for x in (current, minimum)) + result = (current == minimum) if pinned else (current >= minimum) # bool + s = f"WARNING ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed" # string + if hard: + assert result, emojis(s) # assert min requirements met + if verbose and not result: + LOGGER.warning(s) + return result + + +def check_img_size(imgsz, s=32, floor=0): + """Adjusts image size to be divisible by stride `s`, supports int or list/tuple input, returns adjusted size.""" + if isinstance(imgsz, int): # integer i.e. img_size=640 + new_size = max(make_divisible(imgsz, int(s)), floor) + else: # list i.e. img_size=[640, 480] + imgsz = list(imgsz) # convert to list if tuple + new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz] + if new_size != imgsz: + LOGGER.warning(f"WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}") + return new_size + + +def check_imshow(warn=False): + """Checks environment support for image display; warns on failure if `warn=True`.""" + try: + assert not is_jupyter() + assert not is_docker() + cv2.imshow("test", np.zeros((1, 1, 3))) + cv2.waitKey(1) + cv2.destroyAllWindows() + cv2.waitKey(1) + return True + except Exception as e: + if warn: + LOGGER.warning(f"WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n{e}") + return False + + +def check_suffix(file="yolov5s.pt", suffix=(".pt",), msg=""): + """Validates if a file or files have an acceptable suffix, raising an error if not.""" + if file and suffix: + if isinstance(suffix, str): + suffix = [suffix] + for f in file if isinstance(file, (list, tuple)) else [file]: + s = Path(f).suffix.lower() # file suffix + if len(s): + assert s in suffix, f"{msg}{f} acceptable suffix is {suffix}" + + +def check_yaml(file, suffix=(".yaml", ".yml")): + """Searches/downloads a YAML file, verifies its suffix (.yaml or .yml), and returns the file path.""" + return check_file(file, suffix) + + +def check_file(file, suffix=""): + """Searches/downloads a file, checks its suffix (if provided), and returns the file path.""" + check_suffix(file, suffix) # optional + file = str(file) # convert to str() + if os.path.isfile(file) or not file: # exists + return file + elif file.startswith(("http:/", "https:/")): # download + url = file # warning: Pathlib turns :// -> :/ + file = Path(urllib.parse.unquote(file).split("?")[0]).name # '%2F' to '/', split https://url.com/file.txt?auth + if os.path.isfile(file): + LOGGER.info(f"Found {url} locally at {file}") # file already exists + else: + LOGGER.info(f"Downloading {url} to {file}...") + torch.hub.download_url_to_file(url, file) + assert Path(file).exists() and Path(file).stat().st_size > 0, f"File download failed: {url}" # check + return file + elif file.startswith("clearml://"): # ClearML Dataset ID + assert ( + "clearml" in sys.modules + ), "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'." + return file + else: # search + files = [] + for d in "data", "models", "utils": # search directories + files.extend(glob.glob(str(ROOT / d / "**" / file), recursive=True)) # find file + assert len(files), f"File not found: {file}" # assert file was found + assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique + return files[0] # return file + + +def check_font(font=FONT, progress=False): + """Ensures specified font exists or downloads it from Ultralytics assets, optionally displaying progress.""" + font = Path(font) + file = CONFIG_DIR / font.name + if not font.exists() and not file.exists(): + url = f"https://ultralytics.com/assets/{font.name}" + LOGGER.info(f"Downloading {url} to {file}...") + torch.hub.download_url_to_file(url, str(file), progress=progress) + + +def check_dataset(data, autodownload=True): + """Validates and/or auto-downloads a dataset, returning its configuration as a dictionary.""" + + # Download (optional) + extract_dir = "" + if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)): + download(data, dir=f"{DATASETS_DIR}/{Path(data).stem}", unzip=True, delete=False, curl=False, threads=1) + data = next((DATASETS_DIR / Path(data).stem).rglob("*.yaml")) + extract_dir, autodownload = data.parent, False + + # Read yaml (optional) + if isinstance(data, (str, Path)): + data = yaml_load(data) # dictionary + + # Checks + for k in "train", "val", "names": + assert k in data, emojis(f"data.yaml '{k}:' field missing ❌") + if isinstance(data["names"], (list, tuple)): # old array format + data["names"] = dict(enumerate(data["names"])) # convert to dict + assert all(isinstance(k, int) for k in data["names"].keys()), "data.yaml names keys must be integers, i.e. 2: car" + data["nc"] = len(data["names"]) + + # Resolve paths + path = Path(extract_dir or data.get("path") or "") # optional 'path' default to '.' + if not path.is_absolute(): + path = (ROOT / path).resolve() + data["path"] = path # download scripts + for k in "train", "val", "test": + if data.get(k): # prepend path + if isinstance(data[k], str): + x = (path / data[k]).resolve() + if not x.exists() and data[k].startswith("../"): + x = (path / data[k][3:]).resolve() + data[k] = str(x) + else: + data[k] = [str((path / x).resolve()) for x in data[k]] + + # Parse yaml + train, val, test, s = (data.get(x) for x in ("train", "val", "test", "download")) + if val: + val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path + if not all(x.exists() for x in val): + LOGGER.info("\nDataset not found ⚠️, missing paths %s" % [str(x) for x in val if not x.exists()]) + if not s or not autodownload: + raise Exception("Dataset not found ❌") + t = time.time() + if s.startswith("http") and s.endswith(".zip"): # URL + f = Path(s).name # filename + LOGGER.info(f"Downloading {s} to {f}...") + torch.hub.download_url_to_file(s, f) + Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True) # create root + unzip_file(f, path=DATASETS_DIR) # unzip + Path(f).unlink() # remove zip + r = None # success + elif s.startswith("bash "): # bash script + LOGGER.info(f"Running {s} ...") + r = subprocess.run(s, shell=True) + else: # python script + r = exec(s, {"yaml": data}) # return None + dt = f"({round(time.time() - t, 1)}s)" + s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f"failure {dt} ❌" + LOGGER.info(f"Dataset download {s}") + check_font("Arial.ttf" if is_ascii(data["names"]) else "Arial.Unicode.ttf", progress=True) # download fonts + return data # dictionary + + +def check_amp(model): + """Checks PyTorch AMP functionality for a model, returns True if AMP operates correctly, otherwise False.""" + from models.common import AutoShape, DetectMultiBackend + + def amp_allclose(model, im): + """Compares FP32 and AMP model inference outputs, ensuring they are close within a 10% absolute tolerance.""" + m = AutoShape(model, verbose=False) # model + a = m(im).xywhn[0] # FP32 inference + m.amp = True + b = m(im).xywhn[0] # AMP inference + return a.shape == b.shape and torch.allclose(a, b, atol=0.1) # close to 10% absolute tolerance + + prefix = colorstr("AMP: ") + device = next(model.parameters()).device # get model device + if device.type in ("cpu", "mps"): + return False # AMP only used on CUDA devices + f = ROOT / "data" / "images" / "bus.jpg" # image to check + im = f if f.exists() else "https://ultralytics.com/images/bus.jpg" if check_online() else np.ones((640, 640, 3)) + try: + assert amp_allclose(deepcopy(model), im) or amp_allclose(DetectMultiBackend("yolov5n.pt", device), im) + LOGGER.info(f"{prefix}checks passed ✅") + return True + except Exception: + help_url = "https://github.com/ultralytics/yolov5/issues/7908" + LOGGER.warning(f"{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}") + return False + + +def yaml_load(file="data.yaml"): + """Safely loads and returns the contents of a YAML file specified by `file` argument.""" + with open(file, errors="ignore") as f: + return yaml.safe_load(f) + + +def yaml_save(file="data.yaml", data=None): + """Safely saves `data` to a YAML file specified by `file`, converting `Path` objects to strings; `data` is a + dictionary. + """ + if data is None: + data = {} + with open(file, "w") as f: + yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False) + + +def unzip_file(file, path=None, exclude=(".DS_Store", "__MACOSX")): + """Unzips `file` to `path` (default: file's parent), excluding filenames containing any in `exclude` (`.DS_Store`, + `__MACOSX`). + """ + if path is None: + path = Path(file).parent # default path + with ZipFile(file) as zipObj: + for f in zipObj.namelist(): # list all archived filenames in the zip + if all(x not in f for x in exclude): + zipObj.extract(f, path=path) + + +def url2file(url): + """ + Converts a URL string to a valid filename by stripping protocol, domain, and any query parameters. + + Example https://url.com/file.txt?auth -> file.txt + """ + url = str(Path(url)).replace(":/", "://") # Pathlib turns :// -> :/ + return Path(urllib.parse.unquote(url)).name.split("?")[0] # '%2F' to '/', split https://url.com/file.txt?auth + + +def download(url, dir=".", unzip=True, delete=True, curl=False, threads=1, retry=3): + """Downloads and optionally unzips files concurrently, supporting retries and curl fallback.""" + + def download_one(url, dir): + """Downloads a single file from `url` to `dir`, with retry support and optional curl fallback.""" + success = True + if os.path.isfile(url): + f = Path(url) # filename + else: # does not exist + f = dir / Path(url).name + LOGGER.info(f"Downloading {url} to {f}...") + for i in range(retry + 1): + if curl: + success = curl_download(url, f, silent=(threads > 1)) + else: + torch.hub.download_url_to_file(url, f, progress=threads == 1) # torch download + success = f.is_file() + if success: + break + elif i < retry: + LOGGER.warning(f"⚠️ Download failure, retrying {i + 1}/{retry} {url}...") + else: + LOGGER.warning(f"❌ Failed to download {url}...") + + if unzip and success and (f.suffix == ".gz" or is_zipfile(f) or is_tarfile(f)): + LOGGER.info(f"Unzipping {f}...") + if is_zipfile(f): + unzip_file(f, dir) # unzip + elif is_tarfile(f): + subprocess.run(["tar", "xf", f, "--directory", f.parent], check=True) # unzip + elif f.suffix == ".gz": + subprocess.run(["tar", "xfz", f, "--directory", f.parent], check=True) # unzip + if delete: + f.unlink() # remove zip + + dir = Path(dir) + dir.mkdir(parents=True, exist_ok=True) # make directory + if threads > 1: + pool = ThreadPool(threads) + pool.imap(lambda x: download_one(*x), zip(url, repeat(dir))) # multithreaded + pool.close() + pool.join() + else: + for u in [url] if isinstance(url, (str, Path)) else url: + download_one(u, dir) + + +def make_divisible(x, divisor): + """Adjusts `x` to be divisible by `divisor`, returning the nearest greater or equal value.""" + if isinstance(divisor, torch.Tensor): + divisor = int(divisor.max()) # to int + return math.ceil(x / divisor) * divisor + + +def clean_str(s): + """Cleans a string by replacing special characters with underscore, e.g., `clean_str('#example!')` returns + '_example_'. + """ + return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s) + + +def one_cycle(y1=0.0, y2=1.0, steps=100): + """ + Generates a lambda for a sinusoidal ramp from y1 to y2 over 'steps'. + + See https://arxiv.org/pdf/1812.01187.pdf for details. + """ + return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 + + +def colorstr(*input): + """ + Colors a string using ANSI escape codes, e.g., colorstr('blue', 'hello world'). + + See https://en.wikipedia.org/wiki/ANSI_escape_code. + """ + *args, string = input if len(input) > 1 else ("blue", "bold", input[0]) # color arguments, string + colors = { + "black": "\033[30m", # basic colors + "red": "\033[31m", + "green": "\033[32m", + "yellow": "\033[33m", + "blue": "\033[34m", + "magenta": "\033[35m", + "cyan": "\033[36m", + "white": "\033[37m", + "bright_black": "\033[90m", # bright colors + "bright_red": "\033[91m", + "bright_green": "\033[92m", + "bright_yellow": "\033[93m", + "bright_blue": "\033[94m", + "bright_magenta": "\033[95m", + "bright_cyan": "\033[96m", + "bright_white": "\033[97m", + "end": "\033[0m", # misc + "bold": "\033[1m", + "underline": "\033[4m", + } + return "".join(colors[x] for x in args) + f"{string}" + colors["end"] + + +def labels_to_class_weights(labels, nc=80): + """Calculates class weights from labels to handle class imbalance in training; input shape: (n, 5).""" + if labels[0] is None: # no labels loaded + return torch.Tensor() + + labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO + classes = labels[:, 0].astype(int) # labels = [class xywh] + weights = np.bincount(classes, minlength=nc) # occurrences per class + + # Prepend gridpoint count (for uCE training) + # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image + # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start + + weights[weights == 0] = 1 # replace empty bins with 1 + weights = 1 / weights # number of targets per class + weights /= weights.sum() # normalize + return torch.from_numpy(weights).float() + + +def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)): + """Calculates image weights from labels using class weights for weighted sampling.""" + # Usage: index = random.choices(range(n), weights=image_weights, k=1) # weighted image sample + class_counts = np.array([np.bincount(x[:, 0].astype(int), minlength=nc) for x in labels]) + return (class_weights.reshape(1, nc) * class_counts).sum(1) + + +def coco80_to_coco91_class(): + """ + Converts COCO 80-class index to COCO 91-class index used in the paper. + + Reference: https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ + """ + # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n') + # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n') + # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco + # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet + return [ + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 27, + 28, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 67, + 70, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + ] + + +def xyxy2xywh(x): + """Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right.""" + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center + y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center + y[..., 2] = x[..., 2] - x[..., 0] # width + y[..., 3] = x[..., 3] - x[..., 1] # height + return y + + +def xywh2xyxy(x): + """Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right.""" + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x + y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y + y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x + y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y + return y + + +def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): + """Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right.""" + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x + y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y + y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x + y[..., 3] = h * (x[..., 1] + x[..., 3] / 2) + padh # bottom right y + return y + + +def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0): + """Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right.""" + if clip: + clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center + y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center + y[..., 2] = (x[..., 2] - x[..., 0]) / w # width + y[..., 3] = (x[..., 3] - x[..., 1]) / h # height + return y + + +def xyn2xy(x, w=640, h=640, padw=0, padh=0): + """Convert normalized segments into pixel segments, shape (n,2).""" + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = w * x[..., 0] + padw # top left x + y[..., 1] = h * x[..., 1] + padh # top left y + return y + + +def segment2box(segment, width=640, height=640): + """Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy).""" + x, y = segment.T # segment xy + inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) + ( + x, + y, + ) = x[inside], y[inside] + return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy + + +def segments2boxes(segments): + """Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh).""" + boxes = [] + for s in segments: + x, y = s.T # segment xy + boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy + return xyxy2xywh(np.array(boxes)) # cls, xywh + + +def resample_segments(segments, n=1000): + """Resamples an (n,2) segment to a fixed number of points for consistent representation.""" + for i, s in enumerate(segments): + s = np.concatenate((s, s[0:1, :]), axis=0) + x = np.linspace(0, len(s) - 1, n) + xp = np.arange(len(s)) + segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy + return segments + + +def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None): + """Rescales (xyxy) bounding boxes from img1_shape to img0_shape, optionally using provided `ratio_pad`.""" + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + boxes[..., [0, 2]] -= pad[0] # x padding + boxes[..., [1, 3]] -= pad[1] # y padding + boxes[..., :4] /= gain + clip_boxes(boxes, img0_shape) + return boxes + + +def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=False): + """Rescales segment coordinates from img1_shape to img0_shape, optionally normalizing them with custom padding.""" + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + segments[:, 0] -= pad[0] # x padding + segments[:, 1] -= pad[1] # y padding + segments /= gain + clip_segments(segments, img0_shape) + if normalize: + segments[:, 0] /= img0_shape[1] # width + segments[:, 1] /= img0_shape[0] # height + return segments + + +def clip_boxes(boxes, shape): + """Clips bounding box coordinates (xyxy) to fit within the specified image shape (height, width).""" + if isinstance(boxes, torch.Tensor): # faster individually + boxes[..., 0].clamp_(0, shape[1]) # x1 + boxes[..., 1].clamp_(0, shape[0]) # y1 + boxes[..., 2].clamp_(0, shape[1]) # x2 + boxes[..., 3].clamp_(0, shape[0]) # y2 + else: # np.array (faster grouped) + boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2 + boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2 + + +def clip_segments(segments, shape): + """Clips segment coordinates (xy1, xy2, ...) to an image's boundaries given its shape (height, width).""" + if isinstance(segments, torch.Tensor): # faster individually + segments[:, 0].clamp_(0, shape[1]) # x + segments[:, 1].clamp_(0, shape[0]) # y + else: # np.array (faster grouped) + segments[:, 0] = segments[:, 0].clip(0, shape[1]) # x + segments[:, 1] = segments[:, 1].clip(0, shape[0]) # y + + +def non_max_suppression( + prediction, + conf_thres=0.25, + iou_thres=0.45, + classes=None, + agnostic=False, + multi_label=False, + labels=(), + max_det=300, + nm=0, # number of masks +): + """ + Non-Maximum Suppression (NMS) on inference results to reject overlapping detections. + + Returns: + list of detections, on (n,6) tensor per image [xyxy, conf, cls] + """ + + # Checks + assert 0 <= conf_thres <= 1, f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0" + assert 0 <= iou_thres <= 1, f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0" + if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out) + prediction = prediction[0] # select only inference output + + device = prediction.device + mps = "mps" in device.type # Apple MPS + if mps: # MPS not fully supported yet, convert tensors to CPU before NMS + prediction = prediction.cpu() + bs = prediction.shape[0] # batch size + nc = prediction.shape[2] - nm - 5 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Settings + # min_wh = 2 # (pixels) minimum box width and height + max_wh = 7680 # (pixels) maximum box width and height + max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + time_limit = 0.5 + 0.05 * bs # seconds to quit after + redundant = True # require redundant detections + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) + merge = False # use merge-NMS + + t = time.time() + mi = 5 + nc # mask start index + output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + lb = labels[xi] + v = torch.zeros((len(lb), nc + nm + 5), device=x.device) + v[:, :4] = lb[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box/Mask + box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2) + mask = x[:, mi:] # zero columns if no masks + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1) + else: # best class only + conf, j = x[:, 5:mi].max(1, keepdim=True) + x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres] + + # Filter by class + if classes is not None: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence and remove excess boxes + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + i = i[:max_det] # limit detections + if merge and (1 < n < 3e3): # Merge NMS (boxes merged using weighted mean) + # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + + output[xi] = x[i] + if mps: + output[xi] = output[xi].to(device) + if (time.time() - t) > time_limit: + LOGGER.warning(f"WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded") + break # time limit exceeded + + return output + + +def strip_optimizer(f="best.pt", s=""): + """ + Strips optimizer and optionally saves checkpoint to finalize training; arguments are file path 'f' and save path + 's'. + + Example: from utils.general import *; strip_optimizer() + """ + x = torch.load(f, map_location=torch.device("cpu")) + if x.get("ema"): + x["model"] = x["ema"] # replace model with ema + for k in "optimizer", "best_fitness", "ema", "updates": # keys + x[k] = None + x["epoch"] = -1 + x["model"].half() # to FP16 + for p in x["model"].parameters(): + p.requires_grad = False + torch.save(x, s or f) + mb = os.path.getsize(s or f) / 1e6 # filesize + LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB") + + +def print_mutation(keys, results, hyp, save_dir, bucket, prefix=colorstr("evolve: ")): + """Logs evolution results and saves to CSV and YAML in `save_dir`, optionally syncs with `bucket`.""" + evolve_csv = save_dir / "evolve.csv" + evolve_yaml = save_dir / "hyp_evolve.yaml" + keys = tuple(keys) + tuple(hyp.keys()) # [results + hyps] + keys = tuple(x.strip() for x in keys) + vals = results + tuple(hyp.values()) + n = len(keys) + + # Download (optional) + if bucket: + url = f"gs://{bucket}/evolve.csv" + if gsutil_getsize(url) > (evolve_csv.stat().st_size if evolve_csv.exists() else 0): + subprocess.run(["gsutil", "cp", f"{url}", f"{save_dir}"]) # download evolve.csv if larger than local + + # Log to evolve.csv + s = "" if evolve_csv.exists() else (("%20s," * n % keys).rstrip(",") + "\n") # add header + with open(evolve_csv, "a") as f: + f.write(s + ("%20.5g," * n % vals).rstrip(",") + "\n") + + # Save yaml + with open(evolve_yaml, "w") as f: + data = pd.read_csv(evolve_csv, skipinitialspace=True) + data = data.rename(columns=lambda x: x.strip()) # strip keys + i = np.argmax(fitness(data.values[:, :4])) # + generations = len(data) + f.write( + "# YOLOv5 Hyperparameter Evolution Results\n" + + f"# Best generation: {i}\n" + + f"# Last generation: {generations - 1}\n" + + "# " + + ", ".join(f"{x.strip():>20s}" for x in keys[:7]) + + "\n" + + "# " + + ", ".join(f"{x:>20.5g}" for x in data.values[i, :7]) + + "\n\n" + ) + yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False) + + # Print to screen + LOGGER.info( + prefix + + f"{generations} generations finished, current result:\n" + + prefix + + ", ".join(f"{x.strip():>20s}" for x in keys) + + "\n" + + prefix + + ", ".join(f"{x:20.5g}" for x in vals) + + "\n\n" + ) + + if bucket: + subprocess.run(["gsutil", "cp", f"{evolve_csv}", f"{evolve_yaml}", f"gs://{bucket}"]) # upload + + +def apply_classifier(x, model, img, im0): + """Applies second-stage classifier to YOLO outputs, filtering detections by class match.""" + # Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval() + im0 = [im0] if isinstance(im0, np.ndarray) else im0 + for i, d in enumerate(x): # per image + if d is not None and len(d): + d = d.clone() + + # Reshape and pad cutouts + b = xyxy2xywh(d[:, :4]) # boxes + b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square + b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad + d[:, :4] = xywh2xyxy(b).long() + + # Rescale boxes from img_size to im0 size + scale_boxes(img.shape[2:], d[:, :4], im0[i].shape) + + # Classes + pred_cls1 = d[:, 5].long() + ims = [] + for a in d: + cutout = im0[i][int(a[1]) : int(a[3]), int(a[0]) : int(a[2])] + im = cv2.resize(cutout, (224, 224)) # BGR + + im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + ims.append(im) + + pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction + x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections + + return x + + +def increment_path(path, exist_ok=False, sep="", mkdir=False): + """ + Generates an incremented file or directory path if it exists, with optional mkdir; args: path, exist_ok=False, + sep="", mkdir=False. + + Example: runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc + """ + path = Path(path) # os-agnostic + if path.exists() and not exist_ok: + path, suffix = (path.with_suffix(""), path.suffix) if path.is_file() else (path, "") + + # Method 1 + for n in range(2, 9999): + p = f"{path}{sep}{n}{suffix}" # increment path + if not os.path.exists(p): # + break + path = Path(p) + + # Method 2 (deprecated) + # dirs = glob.glob(f"{path}{sep}*") # similar paths + # matches = [re.search(rf"{path.stem}{sep}(\d+)", d) for d in dirs] + # i = [int(m.groups()[0]) for m in matches if m] # indices + # n = max(i) + 1 if i else 2 # increment number + # path = Path(f"{path}{sep}{n}{suffix}") # increment path + + if mkdir: + path.mkdir(parents=True, exist_ok=True) # make directory + + return path + + +# OpenCV Multilanguage-friendly functions ------------------------------------------------------------------------------------ +imshow_ = cv2.imshow # copy to avoid recursion errors + + +def imread(filename, flags=cv2.IMREAD_COLOR): + """Reads an image from a file and returns it as a numpy array, using OpenCV's imdecode to support multilanguage + paths. + """ + return cv2.imdecode(np.fromfile(filename, np.uint8), flags) + + +def imwrite(filename, img): + """Writes an image to a file, returns True on success and False on failure, supports multilanguage paths.""" + try: + cv2.imencode(Path(filename).suffix, img)[1].tofile(filename) + return True + except Exception: + return False + + +def imshow(path, im): + """Displays an image using Unicode path, requires encoded path and image matrix as input.""" + imshow_(path.encode("unicode_escape").decode(), im) + + +if Path(inspect.stack()[0].filename).parent.parent.as_posix() in inspect.stack()[-1].filename: + cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow # redefine + +# Variables ------------------------------------------------------------------------------------------------------------ diff --git a/yolov5/utils/google_app_engine/Dockerfile b/yolov5/utils/google_app_engine/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..0155618f475104e9858b81470339558156c94e13 --- /dev/null +++ b/yolov5/utils/google_app_engine/Dockerfile @@ -0,0 +1,25 @@ +FROM gcr.io/google-appengine/python + +# Create a virtualenv for dependencies. This isolates these packages from +# system-level packages. +# Use -p python3 or -p python3.7 to select python version. Default is version 2. +RUN virtualenv /env -p python3 + +# Setting these environment variables are the same as running +# source /env/bin/activate. +ENV VIRTUAL_ENV /env +ENV PATH /env/bin:$PATH + +RUN apt-get update && apt-get install -y python-opencv + +# Copy the application's requirements.txt and run pip to install all +# dependencies into the virtualenv. +ADD requirements.txt /app/requirements.txt +RUN pip install -r /app/requirements.txt + +# Add the application source code. +ADD . /app + +# Run a WSGI server to serve the application. gunicorn must be declared as +# a dependency in requirements.txt. +CMD gunicorn -b :$PORT main:app diff --git a/yolov5/utils/google_app_engine/additional_requirements.txt b/yolov5/utils/google_app_engine/additional_requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..821c3caf3cbf70b63882d3e2e304dc8b54713224 --- /dev/null +++ b/yolov5/utils/google_app_engine/additional_requirements.txt @@ -0,0 +1,5 @@ +# add these requirements in your app on top of the existing ones +pip==23.3 +Flask==2.3.2 +gunicorn==22.0.0 +werkzeug>=3.0.1 # not directly required, pinned by Snyk to avoid a vulnerability diff --git a/yolov5/utils/google_app_engine/app.yaml b/yolov5/utils/google_app_engine/app.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4c1751f55429d904e0d53492ba6cd72719a21c9a --- /dev/null +++ b/yolov5/utils/google_app_engine/app.yaml @@ -0,0 +1,16 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +runtime: custom +env: flex + +service: yolov5app + +liveness_check: + initial_delay_sec: 600 + +manual_scaling: + instances: 1 +resources: + cpu: 1 + memory_gb: 4 + disk_size_gb: 20 diff --git a/yolov5/utils/loggers/__init__.py b/yolov5/utils/loggers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2bd8583d2adebeb586ce892db6ef23e8c5ba335f --- /dev/null +++ b/yolov5/utils/loggers/__init__.py @@ -0,0 +1,474 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Logging utils.""" + +import json +import os +import warnings +from pathlib import Path + +import pkg_resources as pkg +import torch + +from utils.general import LOGGER, colorstr, cv2 +from utils.loggers.clearml.clearml_utils import ClearmlLogger +from utils.loggers.wandb.wandb_utils import WandbLogger +from utils.plots import plot_images, plot_labels, plot_results +from utils.torch_utils import de_parallel + +LOGGERS = ("csv", "tb", "wandb", "clearml", "comet") # *.csv, TensorBoard, Weights & Biases, ClearML +RANK = int(os.getenv("RANK", -1)) + +try: + from torch.utils.tensorboard import SummaryWriter +except ImportError: + + def SummaryWriter(*args): + """Fall back to SummaryWriter returning None if TensorBoard is not installed.""" + return None # None = SummaryWriter(str) + + +try: + import wandb + + assert hasattr(wandb, "__version__") # verify package import not local dir + if pkg.parse_version(wandb.__version__) >= pkg.parse_version("0.12.2") and RANK in {0, -1}: + try: + wandb_login_success = wandb.login(timeout=30) + except wandb.errors.UsageError: # known non-TTY terminal issue + wandb_login_success = False + if not wandb_login_success: + wandb = None +except (ImportError, AssertionError): + wandb = None + +try: + import clearml + + assert hasattr(clearml, "__version__") # verify package import not local dir +except (ImportError, AssertionError): + clearml = None + +try: + if RANK in {0, -1}: + import comet_ml + + assert hasattr(comet_ml, "__version__") # verify package import not local dir + from utils.loggers.comet import CometLogger + + else: + comet_ml = None +except (ImportError, AssertionError): + comet_ml = None + + +def _json_default(value): + """ + Format `value` for JSON serialization (e.g. unwrap tensors). + + Fall back to strings. + """ + if isinstance(value, torch.Tensor): + try: + value = value.item() + except ValueError: # "only one element tensors can be converted to Python scalars" + pass + return value if isinstance(value, float) else str(value) + + +class Loggers: + # YOLOv5 Loggers class + def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS): + """Initializes loggers for YOLOv5 training and validation metrics, paths, and options.""" + self.save_dir = save_dir + self.weights = weights + self.opt = opt + self.hyp = hyp + self.plots = not opt.noplots # plot results + self.logger = logger # for printing results to console + self.include = include + self.keys = [ + "train/box_loss", + "train/obj_loss", + "train/cls_loss", # train loss + "metrics/precision", + "metrics/recall", + "metrics/mAP_0.5", + "metrics/mAP_0.5:0.95", # metrics + "val/box_loss", + "val/obj_loss", + "val/cls_loss", # val loss + "x/lr0", + "x/lr1", + "x/lr2", + ] # params + self.best_keys = ["best/epoch", "best/precision", "best/recall", "best/mAP_0.5", "best/mAP_0.5:0.95"] + for k in LOGGERS: + setattr(self, k, None) # init empty logger dictionary + self.csv = True # always log to csv + self.ndjson_console = "ndjson_console" in self.include # log ndjson to console + self.ndjson_file = "ndjson_file" in self.include # log ndjson to file + + # Messages + if not comet_ml: + prefix = colorstr("Comet: ") + s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet" + self.logger.info(s) + # TensorBoard + s = self.save_dir + if "tb" in self.include and not self.opt.evolve: + prefix = colorstr("TensorBoard: ") + self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/") + self.tb = SummaryWriter(str(s)) + + # W&B + if wandb and "wandb" in self.include: + self.opt.hyp = self.hyp # add hyperparameters + self.wandb = WandbLogger(self.opt) + else: + self.wandb = None + + # ClearML + if clearml and "clearml" in self.include: + try: + self.clearml = ClearmlLogger(self.opt, self.hyp) + except Exception: + self.clearml = None + prefix = colorstr("ClearML: ") + LOGGER.warning( + f"{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging." + f" See https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration#readme" + ) + + else: + self.clearml = None + + # Comet + if comet_ml and "comet" in self.include: + if isinstance(self.opt.resume, str) and self.opt.resume.startswith("comet://"): + run_id = self.opt.resume.split("/")[-1] + self.comet_logger = CometLogger(self.opt, self.hyp, run_id=run_id) + + else: + self.comet_logger = CometLogger(self.opt, self.hyp) + + else: + self.comet_logger = None + + @property + def remote_dataset(self): + """Fetches dataset dictionary from remote logging services like ClearML, Weights & Biases, or Comet ML.""" + data_dict = None + if self.clearml: + data_dict = self.clearml.data_dict + if self.wandb: + data_dict = self.wandb.data_dict + if self.comet_logger: + data_dict = self.comet_logger.data_dict + + return data_dict + + def on_train_start(self): + """Initializes the training process for Comet ML logger if it's configured.""" + if self.comet_logger: + self.comet_logger.on_train_start() + + def on_pretrain_routine_start(self): + """Invokes pre-training routine start hook for Comet ML logger if available.""" + if self.comet_logger: + self.comet_logger.on_pretrain_routine_start() + + def on_pretrain_routine_end(self, labels, names): + """Callback that runs at the end of pre-training routine, logging label plots if enabled.""" + if self.plots: + plot_labels(labels, names, self.save_dir) + paths = self.save_dir.glob("*labels*.jpg") # training labels + if self.wandb: + self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]}) + if self.comet_logger: + self.comet_logger.on_pretrain_routine_end(paths) + if self.clearml: + for path in paths: + self.clearml.log_plot(title=path.stem, plot_path=path) + + def on_train_batch_end(self, model, ni, imgs, targets, paths, vals): + """Logs training batch end events, plots images, and updates external loggers with batch-end data.""" + log_dict = dict(zip(self.keys[:3], vals)) + # Callback runs on train batch end + # ni: number integrated batches (since train start) + if self.plots: + if ni < 3: + f = self.save_dir / f"train_batch{ni}.jpg" # filename + plot_images(imgs, targets, paths, f) + if ni == 0 and self.tb and not self.opt.sync_bn: + log_tensorboard_graph(self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz)) + if ni == 10 and (self.wandb or self.clearml): + files = sorted(self.save_dir.glob("train*.jpg")) + if self.wandb: + self.wandb.log({"Mosaics": [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]}) + if self.clearml: + self.clearml.log_debug_samples(files, title="Mosaics") + + if self.comet_logger: + self.comet_logger.on_train_batch_end(log_dict, step=ni) + + def on_train_epoch_end(self, epoch): + """Callback that updates the current epoch in Weights & Biases at the end of a training epoch.""" + if self.wandb: + self.wandb.current_epoch = epoch + 1 + + if self.comet_logger: + self.comet_logger.on_train_epoch_end(epoch) + + def on_val_start(self): + """Callback that signals the start of a validation phase to the Comet logger.""" + if self.comet_logger: + self.comet_logger.on_val_start() + + def on_val_image_end(self, pred, predn, path, names, im): + """Callback that logs a validation image and its predictions to WandB or ClearML.""" + if self.wandb: + self.wandb.val_one_image(pred, predn, path, names, im) + if self.clearml: + self.clearml.log_image_with_boxes(path, pred, names, im) + + def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out): + """Logs validation batch results to Comet ML during training at the end of each validation batch.""" + if self.comet_logger: + self.comet_logger.on_val_batch_end(batch_i, im, targets, paths, shapes, out) + + def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix): + """Logs validation results to WandB or ClearML at the end of the validation process.""" + if self.wandb or self.clearml: + files = sorted(self.save_dir.glob("val*.jpg")) + if self.wandb: + self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]}) + if self.clearml: + self.clearml.log_debug_samples(files, title="Validation") + + if self.comet_logger: + self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) + + def on_fit_epoch_end(self, vals, epoch, best_fitness, fi): + """Callback that logs metrics and saves them to CSV or NDJSON at the end of each fit (train+val) epoch.""" + x = dict(zip(self.keys, vals)) + if self.csv: + file = self.save_dir / "results.csv" + n = len(x) + 1 # number of cols + s = "" if file.exists() else (("%20s," * n % tuple(["epoch"] + self.keys)).rstrip(",") + "\n") # add header + with open(file, "a") as f: + f.write(s + ("%20.5g," * n % tuple([epoch] + vals)).rstrip(",") + "\n") + if self.ndjson_console or self.ndjson_file: + json_data = json.dumps(dict(epoch=epoch, **x), default=_json_default) + if self.ndjson_console: + print(json_data) + if self.ndjson_file: + file = self.save_dir / "results.ndjson" + with open(file, "a") as f: + print(json_data, file=f) + + if self.tb: + for k, v in x.items(): + self.tb.add_scalar(k, v, epoch) + elif self.clearml: # log to ClearML if TensorBoard not used + self.clearml.log_scalars(x, epoch) + + if self.wandb: + if best_fitness == fi: + best_results = [epoch] + vals[3:7] + for i, name in enumerate(self.best_keys): + self.wandb.wandb_run.summary[name] = best_results[i] # log best results in the summary + self.wandb.log(x) + self.wandb.end_epoch() + + if self.clearml: + self.clearml.current_epoch_logged_images = set() # reset epoch image limit + self.clearml.current_epoch += 1 + + if self.comet_logger: + self.comet_logger.on_fit_epoch_end(x, epoch=epoch) + + def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): + """Callback that handles model saving events, logging to Weights & Biases or ClearML if enabled.""" + if (epoch + 1) % self.opt.save_period == 0 and not final_epoch and self.opt.save_period != -1: + if self.wandb: + self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) + if self.clearml: + self.clearml.task.update_output_model( + model_path=str(last), model_name="Latest Model", auto_delete_file=False + ) + + if self.comet_logger: + self.comet_logger.on_model_save(last, epoch, final_epoch, best_fitness, fi) + + def on_train_end(self, last, best, epoch, results): + """Callback that runs at the end of training to save plots and log results.""" + if self.plots: + plot_results(file=self.save_dir / "results.csv") # save results.png + files = ["results.png", "confusion_matrix.png", *(f"{x}_curve.png" for x in ("F1", "PR", "P", "R"))] + files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter + self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}") + + if self.tb and not self.clearml: # These images are already captured by ClearML by now, we don't want doubles + for f in files: + self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats="HWC") + + if self.wandb: + self.wandb.log(dict(zip(self.keys[3:10], results))) + self.wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]}) + # Calling wandb.log. TODO: Refactor this into WandbLogger.log_model + if not self.opt.evolve: + wandb.log_artifact( + str(best if best.exists() else last), + type="model", + name=f"run_{self.wandb.wandb_run.id}_model", + aliases=["latest", "best", "stripped"], + ) + self.wandb.finish_run() + + if self.clearml and not self.opt.evolve: + self.clearml.log_summary(dict(zip(self.keys[3:10], results))) + [self.clearml.log_plot(title=f.stem, plot_path=f) for f in files] + self.clearml.log_model( + str(best if best.exists() else last), "Best Model" if best.exists() else "Last Model", epoch + ) + + if self.comet_logger: + final_results = dict(zip(self.keys[3:10], results)) + self.comet_logger.on_train_end(files, self.save_dir, last, best, epoch, final_results) + + def on_params_update(self, params: dict): + """Updates experiment hyperparameters or configurations in WandB, Comet, or ClearML.""" + if self.wandb: + self.wandb.wandb_run.config.update(params, allow_val_change=True) + if self.comet_logger: + self.comet_logger.on_params_update(params) + if self.clearml: + self.clearml.task.connect(params) + + +class GenericLogger: + """ + YOLOv5 General purpose logger for non-task specific logging + Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...) + Arguments + opt: Run arguments + console_logger: Console logger + include: loggers to include + """ + + def __init__(self, opt, console_logger, include=("tb", "wandb", "clearml")): + """Initializes a generic logger with optional TensorBoard, W&B, and ClearML support.""" + self.save_dir = Path(opt.save_dir) + self.include = include + self.console_logger = console_logger + self.csv = self.save_dir / "results.csv" # CSV logger + if "tb" in self.include: + prefix = colorstr("TensorBoard: ") + self.console_logger.info( + f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/" + ) + self.tb = SummaryWriter(str(self.save_dir)) + + if wandb and "wandb" in self.include: + self.wandb = wandb.init( + project=web_project_name(str(opt.project)), name=None if opt.name == "exp" else opt.name, config=opt + ) + else: + self.wandb = None + + if clearml and "clearml" in self.include: + try: + # Hyp is not available in classification mode + hyp = {} if "hyp" not in opt else opt.hyp + self.clearml = ClearmlLogger(opt, hyp) + except Exception: + self.clearml = None + prefix = colorstr("ClearML: ") + LOGGER.warning( + f"{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging." + f" See https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration" + ) + else: + self.clearml = None + + def log_metrics(self, metrics, epoch): + """Logs metrics to CSV, TensorBoard, W&B, and ClearML; `metrics` is a dict, `epoch` is an int.""" + if self.csv: + keys, vals = list(metrics.keys()), list(metrics.values()) + n = len(metrics) + 1 # number of cols + s = "" if self.csv.exists() else (("%23s," * n % tuple(["epoch"] + keys)).rstrip(",") + "\n") # header + with open(self.csv, "a") as f: + f.write(s + ("%23.5g," * n % tuple([epoch] + vals)).rstrip(",") + "\n") + + if self.tb: + for k, v in metrics.items(): + self.tb.add_scalar(k, v, epoch) + + if self.wandb: + self.wandb.log(metrics, step=epoch) + + if self.clearml: + self.clearml.log_scalars(metrics, epoch) + + def log_images(self, files, name="Images", epoch=0): + """Logs images to all loggers with optional naming and epoch specification.""" + files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])] # to Path + files = [f for f in files if f.exists()] # filter by exists + + if self.tb: + for f in files: + self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats="HWC") + + if self.wandb: + self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch) + + if self.clearml: + if name == "Results": + [self.clearml.log_plot(f.stem, f) for f in files] + else: + self.clearml.log_debug_samples(files, title=name) + + def log_graph(self, model, imgsz=(640, 640)): + """Logs model graph to all configured loggers with specified input image size.""" + if self.tb: + log_tensorboard_graph(self.tb, model, imgsz) + + def log_model(self, model_path, epoch=0, metadata=None): + """Logs the model to all configured loggers with optional epoch and metadata.""" + if metadata is None: + metadata = {} + # Log model to all loggers + if self.wandb: + art = wandb.Artifact(name=f"run_{wandb.run.id}_model", type="model", metadata=metadata) + art.add_file(str(model_path)) + wandb.log_artifact(art) + if self.clearml: + self.clearml.log_model(model_path=model_path, model_name=model_path.stem) + + def update_params(self, params): + """Updates logged parameters in WandB and/or ClearML if enabled.""" + if self.wandb: + wandb.run.config.update(params, allow_val_change=True) + if self.clearml: + self.clearml.task.connect(params) + + +def log_tensorboard_graph(tb, model, imgsz=(640, 640)): + """Logs the model graph to TensorBoard with specified image size and model.""" + try: + p = next(model.parameters()) # for device, type + imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz # expand + im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p) # input image (WARNING: must be zeros, not empty) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") # suppress jit trace warning + tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), []) + except Exception as e: + LOGGER.warning(f"WARNING ⚠️ TensorBoard graph visualization failure {e}") + + +def web_project_name(project): + """Converts a local project name to a standardized web project name with optional suffixes.""" + if not project.startswith("runs/train"): + return project + suffix = "-Classify" if project.endswith("-cls") else "-Segment" if project.endswith("-seg") else "" + return f"YOLOv5{suffix}" diff --git a/yolov5/utils/loggers/clearml/README.md b/yolov5/utils/loggers/clearml/README.md new file mode 100644 index 0000000000000000000000000000000000000000..a61e3025445ff2f340a82f79dd312c87c65d17fd --- /dev/null +++ b/yolov5/utils/loggers/clearml/README.md @@ -0,0 +1,222 @@ +# ClearML Integration + +Clear|MLClear|ML + +## About ClearML + +[ClearML](https://cutt.ly/yolov5-tutorial-clearml) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️. + +🔨 Track every YOLOv5 training run in the experiment manager + +🔧 Version and easily access your custom training data with the integrated ClearML Data Versioning Tool + +🔦 Remotely train and monitor your YOLOv5 training runs using ClearML Agent + +🔬 Get the very best mAP using ClearML Hyperparameter Optimization + +🔭 Turn your newly trained YOLOv5 model into an API with just a few commands using ClearML Serving + +And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline! + +![ClearML scalars dashboard](https://github.com/thepycoder/clearml_screenshots/raw/main/experiment_manager_with_compare.gif) + +## 🦾 Setting Things Up + +To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 options to get one: + +Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-tutorial-clearml) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go! + +1. Install the `clearml` python package: + + ```bash + pip install clearml + ``` + +2. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions: + + ```bash + clearml-init + ``` + +That's it! You're done 😎 + +## 🚀 Training YOLOv5 With ClearML + +To enable ClearML experiment tracking, simply install the ClearML pip package. + +```bash +pip install clearml>=1.2.0 +``` + +This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and stored by the ClearML experiment manager. + +If you want to change the `project_name` or `task_name`, use the `--project` and `--name` arguments of the `train.py` script, by default the project will be called `YOLOv5` and the task `Training`. PLEASE NOTE: ClearML uses `/` as a delimiter for subprojects, so be careful when using `/` in your project name! + +```bash +python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache +``` + +or with custom project and task name: + +```bash +python train.py --project my_project --name my_training --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache +``` + +This will capture: + +- Source code + uncommitted changes +- Installed packages +- (Hyper)parameters +- Model files (use `--save-period n` to save a checkpoint every n epochs) +- Console output +- Scalars (mAP_0.5, mAP_0.5:0.95, precision, recall, losses, learning rates, ...) +- General info such as machine details, runtime, creation date etc. +- All produced plots such as label correlogram and confusion matrix +- Images with bounding boxes per epoch +- Mosaic per epoch +- Validation images per epoch +- ... + +That's a lot right? 🤯 Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them! + +There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works! + +## 🔗 Dataset Version Management + +Versioning your data separately from your code is generally a good idea and makes it easy to acquire the latest version too. This repository supports supplying a dataset version ID, and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment! + +![ClearML Dataset Interface](https://github.com/thepycoder/clearml_screenshots/raw/main/clearml_data.gif) + +### Prepare Your Dataset + +The YOLOv5 repository supports a number of different datasets by using yaml files containing their information. By default datasets are downloaded to the `../datasets` folder in relation to the repository root folder. So if you downloaded the `coco128` dataset using the link in the yaml or with the scripts provided by yolov5, you get this folder structure: + +``` +.. +|_ yolov5 +|_ datasets + |_ coco128 + |_ images + |_ labels + |_ LICENSE + |_ README.txt +``` + +But this can be any dataset you wish. Feel free to use your own, as long as you keep to this folder structure. + +Next, ⚠️**copy the corresponding yaml file to the root of the dataset folder**⚠️. This yaml files contains the information ClearML will need to properly use the dataset. You can make this yourself too, of course, just follow the structure of the example yamls. + +Basically we need the following keys: `path`, `train`, `test`, `val`, `nc`, `names`. + +``` +.. +|_ yolov5 +|_ datasets + |_ coco128 + |_ images + |_ labels + |_ coco128.yaml # <---- HERE! + |_ LICENSE + |_ README.txt +``` + +### Upload Your Dataset + +To get this dataset into ClearML as a versioned dataset, go to the dataset root folder and run the following command: + +```bash +cd coco128 +clearml-data sync --project YOLOv5 --name coco128 --folder . +``` + +The command `clearml-data sync` is actually a shorthand command. You could also run these commands one after the other: + +```bash +# Optionally add --parent if you want to base +# this version on another dataset version, so no duplicate files are uploaded! +clearml-data create --name coco128 --project YOLOv5 +clearml-data add --files . +clearml-data close +``` + +### Run Training Using A ClearML Dataset + +Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 🚀 models! + +```bash +python train.py --img 640 --batch 16 --epochs 3 --data clearml:// --weights yolov5s.pt --cache +``` + +## 👀 Hyperparameter Optimization + +Now that we have our experiments and data versioned, it's time to take a look at what we can build on top! + +Using the code information, installed packages and environment details, the experiment itself is now **completely reproducible**. In fact, ClearML allows you to clone an experiment and even change its parameters. We can then just rerun it with these new parameters automatically, this is basically what HPO does! + +To **run hyperparameter optimization locally**, we've included a pre-made script for you. Just make sure a training task has been run at least once, so it is in the ClearML experiment manager, we will essentially clone it and change its hyperparameters. + +You'll need to fill in the ID of this `template task` in the script found at `utils/loggers/clearml/hpo.py` and then just run it :) You can change `task.execute_locally()` to `task.execute()` to put it in a ClearML queue and have a remote agent work on it instead. + +```bash +# To use optuna, install it first, otherwise you can change the optimizer to just be RandomSearch +pip install optuna +python utils/loggers/clearml/hpo.py +``` + +![HPO](https://github.com/thepycoder/clearml_screenshots/raw/main/hpo.png) + +## 🤯 Remote Execution (advanced) + +Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site, or you have some budget to use cloud GPUs. This is where the ClearML Agent comes into play. Check out what the agent can do here: + +- [YouTube video](https://youtu.be/MX3BrXnaULs) +- [Documentation](https://clear.ml/docs/latest/docs/clearml_agent) + +In short: every experiment tracked by the experiment manager contains enough information to reproduce it on a different machine (installed packages, uncommitted changes etc.). So a ClearML agent does just that: it listens to a queue for incoming tasks and when it finds one, it recreates the environment and runs it while still reporting scalars, plots etc. to the experiment manager. + +You can turn any machine (a cloud VM, a local GPU machine, your own laptop ... ) into a ClearML agent by simply running: + +```bash +clearml-agent daemon --queue [--docker] +``` + +### Cloning, Editing And Enqueuing + +With our agent running, we can give it some work. Remember from the HPO section that we can clone a task and edit the hyperparameters? We can do that from the interface too! + +🪄 Clone the experiment by right-clicking it + +🎯 Edit the hyperparameters to what you wish them to be + +⏳ Enqueue the task to any of the queues by right-clicking it + +![Enqueue a task from the UI](https://github.com/thepycoder/clearml_screenshots/raw/main/enqueue.gif) + +### Executing A Task Remotely + +Now you can clone a task like we explained above, or simply mark your current script by adding `task.execute_remotely()` and on execution it will be put into a queue, for the agent to start working on! + +To run the YOLOv5 training script remotely, all you have to do is add this line to the training.py script after the clearml logger has been instantiated: + +```python +# ... +# Loggers +data_dict = None +if RANK in {-1, 0}: + loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance + if loggers.clearml: + loggers.clearml.task.execute_remotely(queue="my_queue") # <------ ADD THIS LINE + # Data_dict is either None is user did not choose for ClearML dataset or is filled in by ClearML + data_dict = loggers.clearml.data_dict +# ... +``` + +When running the training script after this change, python will run the script up until that line, after which it will package the code and send it to the queue instead! + +### Autoscaling workers + +ClearML comes with autoscalers too! This tool will automatically spin up new remote machines in the cloud of your choice (AWS, GCP, Azure) and turn them into ClearML agents for you whenever there are experiments detected in the queue. Once the tasks are processed, the autoscaler will automatically shut down the remote machines, and you stop paying! + +Check out the autoscalers getting started video below. + +[![Watch the video](https://img.youtube.com/vi/j4XVMAaUt3E/0.jpg)](https://youtu.be/j4XVMAaUt3E) diff --git a/yolov5/utils/loggers/clearml/__init__.py b/yolov5/utils/loggers/clearml/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/yolov5/utils/loggers/clearml/clearml_utils.py b/yolov5/utils/loggers/clearml/clearml_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..2b5351ef8533d4362d188326cf9296ab009d3bf9 --- /dev/null +++ b/yolov5/utils/loggers/clearml/clearml_utils.py @@ -0,0 +1,230 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Main Logger class for ClearML experiment tracking.""" + +import glob +import re +from pathlib import Path + +import matplotlib.image as mpimg +import matplotlib.pyplot as plt +import numpy as np +import yaml +from ultralytics.utils.plotting import Annotator, colors + +try: + import clearml + from clearml import Dataset, Task + + assert hasattr(clearml, "__version__") # verify package import not local dir +except (ImportError, AssertionError): + clearml = None + + +def construct_dataset(clearml_info_string): + """Load in a clearml dataset and fill the internal data_dict with its contents.""" + dataset_id = clearml_info_string.replace("clearml://", "") + dataset = Dataset.get(dataset_id=dataset_id) + dataset_root_path = Path(dataset.get_local_copy()) + + # We'll search for the yaml file definition in the dataset + yaml_filenames = list(glob.glob(str(dataset_root_path / "*.yaml")) + glob.glob(str(dataset_root_path / "*.yml"))) + if len(yaml_filenames) > 1: + raise ValueError( + "More than one yaml file was found in the dataset root, cannot determine which one contains " + "the dataset definition this way." + ) + elif not yaml_filenames: + raise ValueError( + "No yaml definition found in dataset root path, check that there is a correct yaml file " + "inside the dataset root path." + ) + with open(yaml_filenames[0]) as f: + dataset_definition = yaml.safe_load(f) + + assert set( + dataset_definition.keys() + ).issuperset( + {"train", "test", "val", "nc", "names"} + ), "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')" + + data_dict = { + "train": ( + str((dataset_root_path / dataset_definition["train"]).resolve()) if dataset_definition["train"] else None + ) + } + data_dict["test"] = ( + str((dataset_root_path / dataset_definition["test"]).resolve()) if dataset_definition["test"] else None + ) + data_dict["val"] = ( + str((dataset_root_path / dataset_definition["val"]).resolve()) if dataset_definition["val"] else None + ) + data_dict["nc"] = dataset_definition["nc"] + data_dict["names"] = dataset_definition["names"] + + return data_dict + + +class ClearmlLogger: + """ + Log training runs, datasets, models, and predictions to ClearML. + + This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default, this information + includes hyperparameters, system configuration and metrics, model metrics, code information and basic data metrics + and analyses. + + By providing additional command line arguments to train.py, datasets, models and predictions can also be logged. + """ + + def __init__(self, opt, hyp): + """ + - Initialize ClearML Task, this object will capture the experiment + - Upload dataset version to ClearML Data if opt.upload_dataset is True + + arguments: + opt (namespace) -- Commandline arguments for this run + hyp (dict) -- Hyperparameters for this run + + """ + self.current_epoch = 0 + # Keep tracked of amount of logged images to enforce a limit + self.current_epoch_logged_images = set() + # Maximum number of images to log to clearML per epoch + self.max_imgs_to_log_per_epoch = 16 + # Get the interval of epochs when bounding box images should be logged + # Only for detection task though! + if "bbox_interval" in opt: + self.bbox_interval = opt.bbox_interval + self.clearml = clearml + self.task = None + self.data_dict = None + if self.clearml: + self.task = Task.init( + project_name="YOLOv5" if str(opt.project).startswith("runs/") else opt.project, + task_name=opt.name if opt.name != "exp" else "Training", + tags=["YOLOv5"], + output_uri=True, + reuse_last_task_id=opt.exist_ok, + auto_connect_frameworks={"pytorch": False, "matplotlib": False}, + # We disconnect pytorch auto-detection, because we added manual model save points in the code + ) + # ClearML's hooks will already grab all general parameters + # Only the hyperparameters coming from the yaml config file + # will have to be added manually! + self.task.connect(hyp, name="Hyperparameters") + self.task.connect(opt, name="Args") + + # Make sure the code is easily remotely runnable by setting the docker image to use by the remote agent + self.task.set_base_docker( + "ultralytics/yolov5:latest", + docker_arguments='--ipc=host -e="CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1"', + docker_setup_bash_script="pip install clearml", + ) + + # Get ClearML Dataset Version if requested + if opt.data.startswith("clearml://"): + # data_dict should have the following keys: + # names, nc (number of classes), test, train, val (all three relative paths to ../datasets) + self.data_dict = construct_dataset(opt.data) + # Set data to data_dict because wandb will crash without this information and opt is the best way + # to give it to them + opt.data = self.data_dict + + def log_scalars(self, metrics, epoch): + """ + Log scalars/metrics to ClearML. + + arguments: + metrics (dict) Metrics in dict format: {"metrics/mAP": 0.8, ...} + epoch (int) iteration number for the current set of metrics + """ + for k, v in metrics.items(): + title, series = k.split("/") + self.task.get_logger().report_scalar(title, series, v, epoch) + + def log_model(self, model_path, model_name, epoch=0): + """ + Log model weights to ClearML. + + arguments: + model_path (PosixPath or str) Path to the model weights + model_name (str) Name of the model visible in ClearML + epoch (int) Iteration / epoch of the model weights + """ + self.task.update_output_model( + model_path=str(model_path), name=model_name, iteration=epoch, auto_delete_file=False + ) + + def log_summary(self, metrics): + """ + Log final metrics to a summary table. + + arguments: + metrics (dict) Metrics in dict format: {"metrics/mAP": 0.8, ...} + """ + for k, v in metrics.items(): + self.task.get_logger().report_single_value(k, v) + + def log_plot(self, title, plot_path): + """ + Log image as plot in the plot section of ClearML. + + arguments: + title (str) Title of the plot + plot_path (PosixPath or str) Path to the saved image file + """ + img = mpimg.imread(plot_path) + fig = plt.figure() + ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect="auto", xticks=[], yticks=[]) # no ticks + ax.imshow(img) + + self.task.get_logger().report_matplotlib_figure(title, "", figure=fig, report_interactive=False) + + def log_debug_samples(self, files, title="Debug Samples"): + """ + Log files (images) as debug samples in the ClearML task. + + arguments: + files (List(PosixPath)) a list of file paths in PosixPath format + title (str) A title that groups together images with the same values + """ + for f in files: + if f.exists(): + it = re.search(r"_batch(\d+)", f.name) + iteration = int(it.groups()[0]) if it else 0 + self.task.get_logger().report_image( + title=title, series=f.name.replace(f"_batch{iteration}", ""), local_path=str(f), iteration=iteration + ) + + def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_threshold=0.25): + """ + Draw the bounding boxes on a single image and report the result as a ClearML debug sample. + + arguments: + image_path (PosixPath) the path the original image file + boxes (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class] + class_names (dict): dict containing mapping of class int to class name + image (Tensor): A torch tensor containing the actual image data + """ + if ( + len(self.current_epoch_logged_images) < self.max_imgs_to_log_per_epoch + and self.current_epoch >= 0 + and (self.current_epoch % self.bbox_interval == 0 and image_path not in self.current_epoch_logged_images) + ): + im = np.ascontiguousarray(np.moveaxis(image.mul(255).clamp(0, 255).byte().cpu().numpy(), 0, 2)) + annotator = Annotator(im=im, pil=True) + for i, (conf, class_nr, box) in enumerate(zip(boxes[:, 4], boxes[:, 5], boxes[:, :4])): + color = colors(i) + + class_name = class_names[int(class_nr)] + confidence_percentage = round(float(conf) * 100, 2) + label = f"{class_name}: {confidence_percentage}%" + + if conf > conf_threshold: + annotator.rectangle(box.cpu().numpy(), outline=color) + annotator.box_label(box.cpu().numpy(), label=label, color=color) + + annotated_image = annotator.result() + self.task.get_logger().report_image( + title="Bounding Boxes", series=image_path.name, iteration=self.current_epoch, image=annotated_image + ) + self.current_epoch_logged_images.add(image_path) diff --git a/yolov5/utils/loggers/clearml/hpo.py b/yolov5/utils/loggers/clearml/hpo.py new file mode 100644 index 0000000000000000000000000000000000000000..5a9be757aac0d76c7f3c1be963037fe42259ce41 --- /dev/null +++ b/yolov5/utils/loggers/clearml/hpo.py @@ -0,0 +1,90 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +from clearml import Task + +# Connecting ClearML with the current process, +# from here on everything is logged automatically +from clearml.automation import HyperParameterOptimizer, UniformParameterRange +from clearml.automation.optuna import OptimizerOptuna + +task = Task.init( + project_name="Hyper-Parameter Optimization", + task_name="YOLOv5", + task_type=Task.TaskTypes.optimizer, + reuse_last_task_id=False, +) + +# Example use case: +optimizer = HyperParameterOptimizer( + # This is the experiment we want to optimize + base_task_id="", + # here we define the hyper-parameters to optimize + # Notice: The parameter name should exactly match what you see in the UI: / + # For Example, here we see in the base experiment a section Named: "General" + # under it a parameter named "batch_size", this becomes "General/batch_size" + # If you have `argparse` for example, then arguments will appear under the "Args" section, + # and you should instead pass "Args/batch_size" + hyper_parameters=[ + UniformParameterRange("Hyperparameters/lr0", min_value=1e-5, max_value=1e-1), + UniformParameterRange("Hyperparameters/lrf", min_value=0.01, max_value=1.0), + UniformParameterRange("Hyperparameters/momentum", min_value=0.6, max_value=0.98), + UniformParameterRange("Hyperparameters/weight_decay", min_value=0.0, max_value=0.001), + UniformParameterRange("Hyperparameters/warmup_epochs", min_value=0.0, max_value=5.0), + UniformParameterRange("Hyperparameters/warmup_momentum", min_value=0.0, max_value=0.95), + UniformParameterRange("Hyperparameters/warmup_bias_lr", min_value=0.0, max_value=0.2), + UniformParameterRange("Hyperparameters/box", min_value=0.02, max_value=0.2), + UniformParameterRange("Hyperparameters/cls", min_value=0.2, max_value=4.0), + UniformParameterRange("Hyperparameters/cls_pw", min_value=0.5, max_value=2.0), + UniformParameterRange("Hyperparameters/obj", min_value=0.2, max_value=4.0), + UniformParameterRange("Hyperparameters/obj_pw", min_value=0.5, max_value=2.0), + UniformParameterRange("Hyperparameters/iou_t", min_value=0.1, max_value=0.7), + UniformParameterRange("Hyperparameters/anchor_t", min_value=2.0, max_value=8.0), + UniformParameterRange("Hyperparameters/fl_gamma", min_value=0.0, max_value=4.0), + UniformParameterRange("Hyperparameters/hsv_h", min_value=0.0, max_value=0.1), + UniformParameterRange("Hyperparameters/hsv_s", min_value=0.0, max_value=0.9), + UniformParameterRange("Hyperparameters/hsv_v", min_value=0.0, max_value=0.9), + UniformParameterRange("Hyperparameters/degrees", min_value=0.0, max_value=45.0), + UniformParameterRange("Hyperparameters/translate", min_value=0.0, max_value=0.9), + UniformParameterRange("Hyperparameters/scale", min_value=0.0, max_value=0.9), + UniformParameterRange("Hyperparameters/shear", min_value=0.0, max_value=10.0), + UniformParameterRange("Hyperparameters/perspective", min_value=0.0, max_value=0.001), + UniformParameterRange("Hyperparameters/flipud", min_value=0.0, max_value=1.0), + UniformParameterRange("Hyperparameters/fliplr", min_value=0.0, max_value=1.0), + UniformParameterRange("Hyperparameters/mosaic", min_value=0.0, max_value=1.0), + UniformParameterRange("Hyperparameters/mixup", min_value=0.0, max_value=1.0), + UniformParameterRange("Hyperparameters/copy_paste", min_value=0.0, max_value=1.0), + ], + # this is the objective metric we want to maximize/minimize + objective_metric_title="metrics", + objective_metric_series="mAP_0.5", + # now we decide if we want to maximize it or minimize it (accuracy we maximize) + objective_metric_sign="max", + # let us limit the number of concurrent experiments, + # this in turn will make sure we don't bombard the scheduler with experiments. + # if we have an auto-scaler connected, this, by proxy, will limit the number of machine + max_number_of_concurrent_tasks=1, + # this is the optimizer class (actually doing the optimization) + # Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band) + optimizer_class=OptimizerOptuna, + # If specified only the top K performing Tasks will be kept, the others will be automatically archived + save_top_k_tasks_only=5, # 5, + compute_time_limit=None, + total_max_jobs=20, + min_iteration_per_job=None, + max_iteration_per_job=None, +) + +# report every 10 seconds, this is way too often, but we are testing here +optimizer.set_report_period(10 / 60) +# You can also use the line below instead to run all the optimizer tasks locally, without using queues or agent +# an_optimizer.start_locally(job_complete_callback=job_complete_callback) +# set the time limit for the optimization process (2 hours) +optimizer.set_time_limit(in_minutes=120.0) +# Start the optimization process in the local environment +optimizer.start_locally() +# wait until process is done (notice we are controlling the optimization process in the background) +optimizer.wait() +# make sure background optimization stopped +optimizer.stop() + +print("We are done, good bye") diff --git a/yolov5/utils/loggers/comet/README.md b/yolov5/utils/loggers/comet/README.md new file mode 100644 index 0000000000000000000000000000000000000000..52f344dba6847b57a31afb40a89217301e213afc --- /dev/null +++ b/yolov5/utils/loggers/comet/README.md @@ -0,0 +1,250 @@ + + +# YOLOv5 with Comet + +This guide will cover how to use YOLOv5 with [Comet](https://bit.ly/yolov5-readme-comet2) + +# About Comet + +Comet builds tools that help data scientists, engineers, and team leaders accelerate and optimize machine learning and deep learning models. + +Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes! + +# Getting Started + +## Install Comet + +```shell +pip install comet_ml +``` + +## Configure Comet Credentials + +There are two ways to configure Comet with YOLOv5. + +You can either set your credentials through environment variables + +**Environment Variables** + +```shell +export COMET_API_KEY= +export COMET_PROJECT_NAME= # This will default to 'yolov5' +``` + +Or create a `.comet.config` file in your working directory and set your credentials there. + +**Comet Configuration File** + +``` +[comet] +api_key= +project_name= # This will default to 'yolov5' +``` + +## Run the Training Script + +```shell +# Train YOLOv5s on COCO128 for 5 epochs +python train.py --img 640 --batch 16 --epochs 5 --data coco128.yaml --weights yolov5s.pt +``` + +That's it! Comet will automatically log your hyperparameters, command line arguments, training and validation metrics. You can visualize and analyze your runs in the Comet UI + +yolo-ui + +# Try out an Example! + +Check out an example of a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) + +Or better yet, try it out yourself in this Colab Notebook + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-training/yolov5/notebooks/Comet_and_YOLOv5.ipynb) + +# Log automatically + +By default, Comet will log the following items + +## Metrics + +- Box Loss, Object Loss, Classification Loss for the training and validation data +- mAP_0.5, mAP_0.5:0.95 metrics for the validation data. +- Precision and Recall for the validation data + +## Parameters + +- Model Hyperparameters +- All parameters passed through the command line options + +## Visualizations + +- Confusion Matrix of the model predictions on the validation data +- Plots for the PR and F1 curves across all classes +- Correlogram of the Class Labels + +# Configure Comet Logging + +Comet can be configured to log additional data either through command line flags passed to the training script or through environment variables. + +```shell +export COMET_MODE=online # Set whether to run Comet in 'online' or 'offline' mode. Defaults to online +export COMET_MODEL_NAME= #Set the name for the saved model. Defaults to yolov5 +export COMET_LOG_CONFUSION_MATRIX=false # Set to disable logging a Comet Confusion Matrix. Defaults to true +export COMET_MAX_IMAGE_UPLOADS= # Controls how many total image predictions to log to Comet. Defaults to 100. +export COMET_LOG_PER_CLASS_METRICS=true # Set to log evaluation metrics for each detected class at the end of training. Defaults to false +export COMET_DEFAULT_CHECKPOINT_FILENAME= # Set this if you would like to resume training from a different checkpoint. Defaults to 'last.pt' +export COMET_LOG_BATCH_LEVEL_METRICS=true # Set this if you would like to log training metrics at the batch level. Defaults to false. +export COMET_LOG_PREDICTIONS=true # Set this to false to disable logging model predictions +``` + +## Logging Checkpoints with Comet + +Logging Models to Comet is disabled by default. To enable it, pass the `save-period` argument to the training script. This will save the logged checkpoints to Comet based on the interval value provided by `save-period` + +```shell +python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt \ +--save-period 1 +``` + +## Logging Model Predictions + +By default, model predictions (images, ground truth labels and bounding boxes) will be logged to Comet. + +You can control the frequency of logged predictions and the associated images by passing the `bbox_interval` command line argument. Predictions can be visualized using Comet's Object Detection Custom Panel. This frequency corresponds to every Nth batch of data per epoch. In the example below, we are logging every 2nd batch of data for each epoch. + +**Note:** The YOLOv5 validation dataloader will default to a batch size of 32, so you will have to set the logging frequency accordingly. + +Here is an [example project using the Panel](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) + +```shell +python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt \ +--bbox_interval 2 +``` + +### Controlling the number of Prediction Images logged to Comet + +When logging predictions from YOLOv5, Comet will log the images associated with each set of predictions. By default a maximum of 100 validation images are logged. You can increase or decrease this number using the `COMET_MAX_IMAGE_UPLOADS` environment variable. + +```shell +env COMET_MAX_IMAGE_UPLOADS=200 python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt \ +--bbox_interval 1 +``` + +### Logging Class Level Metrics + +Use the `COMET_LOG_PER_CLASS_METRICS` environment variable to log mAP, precision, recall, f1 for each class. + +```shell +env COMET_LOG_PER_CLASS_METRICS=true python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt +``` + +## Uploading a Dataset to Comet Artifacts + +If you would like to store your data using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github), you can do so using the `upload_dataset` flag. + +The dataset be organized in the way described in the [YOLOv5 documentation](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/). The dataset config `yaml` file must follow the same format as that of the `coco128.yaml` file. + +```shell +python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt \ +--upload_dataset +``` + +You can find the uploaded dataset in the Artifacts tab in your Comet Workspace artifact-1 + +You can preview the data directly in the Comet UI. artifact-2 + +Artifacts are versioned and also support adding metadata about the dataset. Comet will automatically log the metadata from your dataset `yaml` file artifact-3 + +### Using a saved Artifact + +If you would like to use a dataset from Comet Artifacts, set the `path` variable in your dataset `yaml` file to point to the following Artifact resource URL. + +``` +# contents of artifact.yaml file +path: "comet:///:" +``` + +Then pass this file to your training script in the following way + +```shell +python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data artifact.yaml \ +--weights yolov5s.pt +``` + +Artifacts also allow you to track the lineage of data as it flows through your Experimentation workflow. Here you can see a graph that shows you all the experiments that have used your uploaded dataset. artifact-4 + +## Resuming a Training Run + +If your training run is interrupted for any reason, e.g. disrupted internet connection, you can resume the run using the `resume` flag and the Comet Run Path. + +The Run Path has the following format `comet:////`. + +This will restore the run to its state before the interruption, which includes restoring the model from a checkpoint, restoring all hyperparameters and training arguments and downloading Comet dataset Artifacts if they were used in the original run. The resumed run will continue logging to the existing Experiment in the Comet UI + +```shell +python train.py \ +--resume "comet://" +``` + +## Hyperparameter Search with the Comet Optimizer + +YOLOv5 is also integrated with Comet's Optimizer, making is simple to visualize hyperparameter sweeps in the Comet UI. + +### Configuring an Optimizer Sweep + +To configure the Comet Optimizer, you will have to create a JSON file with the information about the sweep. An example file has been provided in `utils/loggers/comet/optimizer_config.json` + +```shell +python utils/loggers/comet/hpo.py \ + --comet_optimizer_config "utils/loggers/comet/optimizer_config.json" +``` + +The `hpo.py` script accepts the same arguments as `train.py`. If you wish to pass additional arguments to your sweep simply add them after the script. + +```shell +python utils/loggers/comet/hpo.py \ + --comet_optimizer_config "utils/loggers/comet/optimizer_config.json" \ + --save-period 1 \ + --bbox_interval 1 +``` + +### Running a Sweep in Parallel + +```shell +comet optimizer -j utils/loggers/comet/hpo.py \ + utils/loggers/comet/optimizer_config.json" +``` + +### Visualizing Results + +Comet provides a number of ways to visualize the results of your sweep. Take a look at a [project with a completed sweep here](https://www.comet.com/examples/comet-example-yolov5/view/PrlArHGuuhDTKC1UuBmTtOSXD/panels?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) + +hyperparameter-yolo diff --git a/yolov5/utils/loggers/comet/__init__.py b/yolov5/utils/loggers/comet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..846dcb42a225110cba43200372e4b0ac295ad4ea --- /dev/null +++ b/yolov5/utils/loggers/comet/__init__.py @@ -0,0 +1,551 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +import glob +import json +import logging +import os +import sys +from pathlib import Path + +logger = logging.getLogger(__name__) + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[3] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH + +try: + import comet_ml + + # Project Configuration + config = comet_ml.config.get_config() + COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5") +except ImportError: + comet_ml = None + COMET_PROJECT_NAME = None + +import PIL +import torch +import torchvision.transforms as T +import yaml + +from utils.dataloaders import img2label_paths +from utils.general import check_dataset, scale_boxes, xywh2xyxy +from utils.metrics import box_iou + +COMET_PREFIX = "comet://" + +COMET_MODE = os.getenv("COMET_MODE", "online") + +# Model Saving Settings +COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5") + +# Dataset Artifact Settings +COMET_UPLOAD_DATASET = os.getenv("COMET_UPLOAD_DATASET", "false").lower() == "true" + +# Evaluation Settings +COMET_LOG_CONFUSION_MATRIX = os.getenv("COMET_LOG_CONFUSION_MATRIX", "true").lower() == "true" +COMET_LOG_PREDICTIONS = os.getenv("COMET_LOG_PREDICTIONS", "true").lower() == "true" +COMET_MAX_IMAGE_UPLOADS = int(os.getenv("COMET_MAX_IMAGE_UPLOADS", 100)) + +# Confusion Matrix Settings +CONF_THRES = float(os.getenv("CONF_THRES", 0.001)) +IOU_THRES = float(os.getenv("IOU_THRES", 0.6)) + +# Batch Logging Settings +COMET_LOG_BATCH_METRICS = os.getenv("COMET_LOG_BATCH_METRICS", "false").lower() == "true" +COMET_BATCH_LOGGING_INTERVAL = os.getenv("COMET_BATCH_LOGGING_INTERVAL", 1) +COMET_PREDICTION_LOGGING_INTERVAL = os.getenv("COMET_PREDICTION_LOGGING_INTERVAL", 1) +COMET_LOG_PER_CLASS_METRICS = os.getenv("COMET_LOG_PER_CLASS_METRICS", "false").lower() == "true" + +RANK = int(os.getenv("RANK", -1)) + +to_pil = T.ToPILImage() + + +class CometLogger: + """Log metrics, parameters, source code, models and much more with Comet.""" + + def __init__(self, opt, hyp, run_id=None, job_type="Training", **experiment_kwargs) -> None: + """Initializes CometLogger with given options, hyperparameters, run ID, job type, and additional experiment + arguments. + """ + self.job_type = job_type + self.opt = opt + self.hyp = hyp + + # Comet Flags + self.comet_mode = COMET_MODE + + self.save_model = opt.save_period > -1 + self.model_name = COMET_MODEL_NAME + + # Batch Logging Settings + self.log_batch_metrics = COMET_LOG_BATCH_METRICS + self.comet_log_batch_interval = COMET_BATCH_LOGGING_INTERVAL + + # Dataset Artifact Settings + self.upload_dataset = self.opt.upload_dataset or COMET_UPLOAD_DATASET + self.resume = self.opt.resume + + # Default parameters to pass to Experiment objects + self.default_experiment_kwargs = { + "log_code": False, + "log_env_gpu": True, + "log_env_cpu": True, + "project_name": COMET_PROJECT_NAME, + } + self.default_experiment_kwargs.update(experiment_kwargs) + self.experiment = self._get_experiment(self.comet_mode, run_id) + self.experiment.set_name(self.opt.name) + + self.data_dict = self.check_dataset(self.opt.data) + self.class_names = self.data_dict["names"] + self.num_classes = self.data_dict["nc"] + + self.logged_images_count = 0 + self.max_images = COMET_MAX_IMAGE_UPLOADS + + if run_id is None: + self.experiment.log_other("Created from", "YOLOv5") + if not isinstance(self.experiment, comet_ml.OfflineExperiment): + workspace, project_name, experiment_id = self.experiment.url.split("/")[-3:] + self.experiment.log_other( + "Run Path", + f"{workspace}/{project_name}/{experiment_id}", + ) + self.log_parameters(vars(opt)) + self.log_parameters(self.opt.hyp) + self.log_asset_data( + self.opt.hyp, + name="hyperparameters.json", + metadata={"type": "hyp-config-file"}, + ) + self.log_asset( + f"{self.opt.save_dir}/opt.yaml", + metadata={"type": "opt-config-file"}, + ) + + self.comet_log_confusion_matrix = COMET_LOG_CONFUSION_MATRIX + + if hasattr(self.opt, "conf_thres"): + self.conf_thres = self.opt.conf_thres + else: + self.conf_thres = CONF_THRES + if hasattr(self.opt, "iou_thres"): + self.iou_thres = self.opt.iou_thres + else: + self.iou_thres = IOU_THRES + + self.log_parameters({"val_iou_threshold": self.iou_thres, "val_conf_threshold": self.conf_thres}) + + self.comet_log_predictions = COMET_LOG_PREDICTIONS + if self.opt.bbox_interval == -1: + self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10 + else: + self.comet_log_prediction_interval = self.opt.bbox_interval + + if self.comet_log_predictions: + self.metadata_dict = {} + self.logged_image_names = [] + + self.comet_log_per_class_metrics = COMET_LOG_PER_CLASS_METRICS + + self.experiment.log_others( + { + "comet_mode": COMET_MODE, + "comet_max_image_uploads": COMET_MAX_IMAGE_UPLOADS, + "comet_log_per_class_metrics": COMET_LOG_PER_CLASS_METRICS, + "comet_log_batch_metrics": COMET_LOG_BATCH_METRICS, + "comet_log_confusion_matrix": COMET_LOG_CONFUSION_MATRIX, + "comet_model_name": COMET_MODEL_NAME, + } + ) + + # Check if running the Experiment with the Comet Optimizer + if hasattr(self.opt, "comet_optimizer_id"): + self.experiment.log_other("optimizer_id", self.opt.comet_optimizer_id) + self.experiment.log_other("optimizer_objective", self.opt.comet_optimizer_objective) + self.experiment.log_other("optimizer_metric", self.opt.comet_optimizer_metric) + self.experiment.log_other("optimizer_parameters", json.dumps(self.hyp)) + + def _get_experiment(self, mode, experiment_id=None): + """Returns a new or existing Comet.ml experiment based on mode and optional experiment_id.""" + if mode == "offline": + return ( + comet_ml.ExistingOfflineExperiment( + previous_experiment=experiment_id, + **self.default_experiment_kwargs, + ) + if experiment_id is not None + else comet_ml.OfflineExperiment( + **self.default_experiment_kwargs, + ) + ) + try: + if experiment_id is not None: + return comet_ml.ExistingExperiment( + previous_experiment=experiment_id, + **self.default_experiment_kwargs, + ) + + return comet_ml.Experiment(**self.default_experiment_kwargs) + + except ValueError: + logger.warning( + "COMET WARNING: " + "Comet credentials have not been set. " + "Comet will default to offline logging. " + "Please set your credentials to enable online logging." + ) + return self._get_experiment("offline", experiment_id) + + return + + def log_metrics(self, log_dict, **kwargs): + """Logs metrics to the current experiment, accepting a dictionary of metric names and values.""" + self.experiment.log_metrics(log_dict, **kwargs) + + def log_parameters(self, log_dict, **kwargs): + """Logs parameters to the current experiment, accepting a dictionary of parameter names and values.""" + self.experiment.log_parameters(log_dict, **kwargs) + + def log_asset(self, asset_path, **kwargs): + """Logs a file or directory as an asset to the current experiment.""" + self.experiment.log_asset(asset_path, **kwargs) + + def log_asset_data(self, asset, **kwargs): + """Logs in-memory data as an asset to the current experiment, with optional kwargs.""" + self.experiment.log_asset_data(asset, **kwargs) + + def log_image(self, img, **kwargs): + """Logs an image to the current experiment with optional kwargs.""" + self.experiment.log_image(img, **kwargs) + + def log_model(self, path, opt, epoch, fitness_score, best_model=False): + """Logs model checkpoint to experiment with path, options, epoch, fitness, and best model flag.""" + if not self.save_model: + return + + model_metadata = { + "fitness_score": fitness_score[-1], + "epochs_trained": epoch + 1, + "save_period": opt.save_period, + "total_epochs": opt.epochs, + } + + model_files = glob.glob(f"{path}/*.pt") + for model_path in model_files: + name = Path(model_path).name + + self.experiment.log_model( + self.model_name, + file_or_folder=model_path, + file_name=name, + metadata=model_metadata, + overwrite=True, + ) + + def check_dataset(self, data_file): + """Validates the dataset configuration by loading the YAML file specified in `data_file`.""" + with open(data_file) as f: + data_config = yaml.safe_load(f) + + path = data_config.get("path") + if path and path.startswith(COMET_PREFIX): + path = data_config["path"].replace(COMET_PREFIX, "") + return self.download_dataset_artifact(path) + self.log_asset(self.opt.data, metadata={"type": "data-config-file"}) + + return check_dataset(data_file) + + def log_predictions(self, image, labelsn, path, shape, predn): + """Logs predictions with IOU filtering, given image, labels, path, shape, and predictions.""" + if self.logged_images_count >= self.max_images: + return + detections = predn[predn[:, 4] > self.conf_thres] + iou = box_iou(labelsn[:, 1:], detections[:, :4]) + mask, _ = torch.where(iou > self.iou_thres) + if len(mask) == 0: + return + + filtered_detections = detections[mask] + filtered_labels = labelsn[mask] + + image_id = path.split("/")[-1].split(".")[0] + image_name = f"{image_id}_curr_epoch_{self.experiment.curr_epoch}" + if image_name not in self.logged_image_names: + native_scale_image = PIL.Image.open(path) + self.log_image(native_scale_image, name=image_name) + self.logged_image_names.append(image_name) + + metadata = [ + { + "label": f"{self.class_names[int(cls)]}-gt", + "score": 100, + "box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]}, + } + for cls, *xyxy in filtered_labels.tolist() + ] + metadata.extend( + { + "label": f"{self.class_names[int(cls)]}", + "score": conf * 100, + "box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]}, + } + for *xyxy, conf, cls in filtered_detections.tolist() + ) + self.metadata_dict[image_name] = metadata + self.logged_images_count += 1 + + return + + def preprocess_prediction(self, image, labels, shape, pred): + """Processes prediction data, resizing labels and adding dataset metadata.""" + nl, _ = labels.shape[0], pred.shape[0] + + # Predictions + if self.opt.single_cls: + pred[:, 5] = 0 + + predn = pred.clone() + scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) + + labelsn = None + if nl: + tbox = xywh2xyxy(labels[:, 1:5]) # target boxes + scale_boxes(image.shape[1:], tbox, shape[0], shape[1]) # native-space labels + labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels + scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) # native-space pred + + return predn, labelsn + + def add_assets_to_artifact(self, artifact, path, asset_path, split): + """Adds image and label assets to a wandb artifact given dataset split and paths.""" + img_paths = sorted(glob.glob(f"{asset_path}/*")) + label_paths = img2label_paths(img_paths) + + for image_file, label_file in zip(img_paths, label_paths): + image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file]) + + try: + artifact.add( + image_file, + logical_path=image_logical_path, + metadata={"split": split}, + ) + artifact.add( + label_file, + logical_path=label_logical_path, + metadata={"split": split}, + ) + except ValueError as e: + logger.error("COMET ERROR: Error adding file to Artifact. Skipping file.") + logger.error(f"COMET ERROR: {e}") + continue + + return artifact + + def upload_dataset_artifact(self): + """Uploads a YOLOv5 dataset as an artifact to the Comet.ml platform.""" + dataset_name = self.data_dict.get("dataset_name", "yolov5-dataset") + path = str((ROOT / Path(self.data_dict["path"])).resolve()) + + metadata = self.data_dict.copy() + for key in ["train", "val", "test"]: + split_path = metadata.get(key) + if split_path is not None: + metadata[key] = split_path.replace(path, "") + + artifact = comet_ml.Artifact(name=dataset_name, artifact_type="dataset", metadata=metadata) + for key in metadata.keys(): + if key in ["train", "val", "test"]: + if isinstance(self.upload_dataset, str) and (key != self.upload_dataset): + continue + + asset_path = self.data_dict.get(key) + if asset_path is not None: + artifact = self.add_assets_to_artifact(artifact, path, asset_path, key) + + self.experiment.log_artifact(artifact) + + return + + def download_dataset_artifact(self, artifact_path): + """Downloads a dataset artifact to a specified directory using the experiment's logged artifact.""" + logged_artifact = self.experiment.get_artifact(artifact_path) + artifact_save_dir = str(Path(self.opt.save_dir) / logged_artifact.name) + logged_artifact.download(artifact_save_dir) + + metadata = logged_artifact.metadata + data_dict = metadata.copy() + data_dict["path"] = artifact_save_dir + + metadata_names = metadata.get("names") + if isinstance(metadata_names, dict): + data_dict["names"] = {int(k): v for k, v in metadata.get("names").items()} + elif isinstance(metadata_names, list): + data_dict["names"] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)} + else: + raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary" + + return self.update_data_paths(data_dict) + + def update_data_paths(self, data_dict): + """Updates data paths in the dataset dictionary, defaulting 'path' to an empty string if not present.""" + path = data_dict.get("path", "") + + for split in ["train", "val", "test"]: + if data_dict.get(split): + split_path = data_dict.get(split) + data_dict[split] = ( + f"{path}/{split_path}" if isinstance(split, str) else [f"{path}/{x}" for x in split_path] + ) + + return data_dict + + def on_pretrain_routine_end(self, paths): + """Called at the end of pretraining routine to handle paths if training is not being resumed.""" + if self.opt.resume: + return + + for path in paths: + self.log_asset(str(path)) + + if self.upload_dataset and not self.resume: + self.upload_dataset_artifact() + + return + + def on_train_start(self): + """Logs hyperparameters at the start of training.""" + self.log_parameters(self.hyp) + + def on_train_epoch_start(self): + """Called at the start of each training epoch.""" + return + + def on_train_epoch_end(self, epoch): + """Updates the current epoch in the experiment tracking at the end of each epoch.""" + self.experiment.curr_epoch = epoch + + return + + def on_train_batch_start(self): + """Called at the start of each training batch.""" + return + + def on_train_batch_end(self, log_dict, step): + """Callback function that updates and logs metrics at the end of each training batch if conditions are met.""" + self.experiment.curr_step = step + if self.log_batch_metrics and (step % self.comet_log_batch_interval == 0): + self.log_metrics(log_dict, step=step) + + return + + def on_train_end(self, files, save_dir, last, best, epoch, results): + """Logs metadata and optionally saves model files at the end of training.""" + if self.comet_log_predictions: + curr_epoch = self.experiment.curr_epoch + self.experiment.log_asset_data(self.metadata_dict, "image-metadata.json", epoch=curr_epoch) + + for f in files: + self.log_asset(f, metadata={"epoch": epoch}) + self.log_asset(f"{save_dir}/results.csv", metadata={"epoch": epoch}) + + if not self.opt.evolve: + model_path = str(best if best.exists() else last) + name = Path(model_path).name + if self.save_model: + self.experiment.log_model( + self.model_name, + file_or_folder=model_path, + file_name=name, + overwrite=True, + ) + + # Check if running Experiment with Comet Optimizer + if hasattr(self.opt, "comet_optimizer_id"): + metric = results.get(self.opt.comet_optimizer_metric) + self.experiment.log_other("optimizer_metric_value", metric) + + self.finish_run() + + def on_val_start(self): + """Called at the start of validation, currently a placeholder with no functionality.""" + return + + def on_val_batch_start(self): + """Placeholder called at the start of a validation batch with no current functionality.""" + return + + def on_val_batch_end(self, batch_i, images, targets, paths, shapes, outputs): + """Callback executed at the end of a validation batch, conditionally logs predictions to Comet ML.""" + if not (self.comet_log_predictions and ((batch_i + 1) % self.comet_log_prediction_interval == 0)): + return + + for si, pred in enumerate(outputs): + if len(pred) == 0: + continue + + image = images[si] + labels = targets[targets[:, 0] == si, 1:] + shape = shapes[si] + path = paths[si] + predn, labelsn = self.preprocess_prediction(image, labels, shape, pred) + if labelsn is not None: + self.log_predictions(image, labelsn, path, shape, predn) + + return + + def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix): + """Logs per-class metrics to Comet.ml after validation if enabled and more than one class exists.""" + if self.comet_log_per_class_metrics and self.num_classes > 1: + for i, c in enumerate(ap_class): + class_name = self.class_names[c] + self.experiment.log_metrics( + { + "mAP@.5": ap50[i], + "mAP@.5:.95": ap[i], + "precision": p[i], + "recall": r[i], + "f1": f1[i], + "true_positives": tp[i], + "false_positives": fp[i], + "support": nt[c], + }, + prefix=class_name, + ) + + if self.comet_log_confusion_matrix: + epoch = self.experiment.curr_epoch + class_names = list(self.class_names.values()) + class_names.append("background") + num_classes = len(class_names) + + self.experiment.log_confusion_matrix( + matrix=confusion_matrix.matrix, + max_categories=num_classes, + labels=class_names, + epoch=epoch, + column_label="Actual Category", + row_label="Predicted Category", + file_name=f"confusion-matrix-epoch-{epoch}.json", + ) + + def on_fit_epoch_end(self, result, epoch): + """Logs metrics at the end of each training epoch.""" + self.log_metrics(result, epoch=epoch) + + def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): + """Callback to save model checkpoints periodically if conditions are met.""" + if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1: + self.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) + + def on_params_update(self, params): + """Logs updated parameters during training.""" + self.log_parameters(params) + + def finish_run(self): + """Ends the current experiment and logs its completion.""" + self.experiment.end() diff --git a/yolov5/utils/loggers/comet/comet_utils.py b/yolov5/utils/loggers/comet/comet_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..cf936ab484533c427e8b5f0b5ca7ca109ee0a275 --- /dev/null +++ b/yolov5/utils/loggers/comet/comet_utils.py @@ -0,0 +1,151 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +import logging +import os +from urllib.parse import urlparse + +try: + import comet_ml +except ImportError: + comet_ml = None + +import yaml + +logger = logging.getLogger(__name__) + +COMET_PREFIX = "comet://" +COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5") +COMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv("COMET_DEFAULT_CHECKPOINT_FILENAME", "last.pt") + + +def download_model_checkpoint(opt, experiment): + """Downloads YOLOv5 model checkpoint from Comet ML experiment, updating `opt.weights` with download path.""" + model_dir = f"{opt.project}/{experiment.name}" + os.makedirs(model_dir, exist_ok=True) + + model_name = COMET_MODEL_NAME + model_asset_list = experiment.get_model_asset_list(model_name) + + if len(model_asset_list) == 0: + logger.error(f"COMET ERROR: No checkpoints found for model name : {model_name}") + return + + model_asset_list = sorted( + model_asset_list, + key=lambda x: x["step"], + reverse=True, + ) + logged_checkpoint_map = {asset["fileName"]: asset["assetId"] for asset in model_asset_list} + + resource_url = urlparse(opt.weights) + checkpoint_filename = resource_url.query + + if checkpoint_filename: + asset_id = logged_checkpoint_map.get(checkpoint_filename) + else: + asset_id = logged_checkpoint_map.get(COMET_DEFAULT_CHECKPOINT_FILENAME) + checkpoint_filename = COMET_DEFAULT_CHECKPOINT_FILENAME + + if asset_id is None: + logger.error(f"COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment") + return + + try: + logger.info(f"COMET INFO: Downloading checkpoint {checkpoint_filename}") + asset_filename = checkpoint_filename + + model_binary = experiment.get_asset(asset_id, return_type="binary", stream=False) + model_download_path = f"{model_dir}/{asset_filename}" + with open(model_download_path, "wb") as f: + f.write(model_binary) + + opt.weights = model_download_path + + except Exception as e: + logger.warning("COMET WARNING: Unable to download checkpoint from Comet") + logger.exception(e) + + +def set_opt_parameters(opt, experiment): + """ + Update the opts Namespace with parameters from Comet's ExistingExperiment when resuming a run. + + Args: + opt (argparse.Namespace): Namespace of command line options + experiment (comet_ml.APIExperiment): Comet API Experiment object + """ + asset_list = experiment.get_asset_list() + resume_string = opt.resume + + for asset in asset_list: + if asset["fileName"] == "opt.yaml": + asset_id = asset["assetId"] + asset_binary = experiment.get_asset(asset_id, return_type="binary", stream=False) + opt_dict = yaml.safe_load(asset_binary) + for key, value in opt_dict.items(): + setattr(opt, key, value) + opt.resume = resume_string + + # Save hyperparameters to YAML file + # Necessary to pass checks in training script + save_dir = f"{opt.project}/{experiment.name}" + os.makedirs(save_dir, exist_ok=True) + + hyp_yaml_path = f"{save_dir}/hyp.yaml" + with open(hyp_yaml_path, "w") as f: + yaml.dump(opt.hyp, f) + opt.hyp = hyp_yaml_path + + +def check_comet_weights(opt): + """ + Downloads model weights from Comet and updates the weights path to point to saved weights location. + + Args: + opt (argparse.Namespace): Command Line arguments passed + to YOLOv5 training script + + Returns: + None/bool: Return True if weights are successfully downloaded + else return None + """ + if comet_ml is None: + return + + if isinstance(opt.weights, str) and opt.weights.startswith(COMET_PREFIX): + api = comet_ml.API() + resource = urlparse(opt.weights) + experiment_path = f"{resource.netloc}{resource.path}" + experiment = api.get(experiment_path) + download_model_checkpoint(opt, experiment) + return True + + return None + + +def check_comet_resume(opt): + """ + Restores run parameters to its original state based on the model checkpoint and logged Experiment parameters. + + Args: + opt (argparse.Namespace): Command Line arguments passed + to YOLOv5 training script + + Returns: + None/bool: Return True if the run is restored successfully + else return None + """ + if comet_ml is None: + return + + if isinstance(opt.resume, str) and opt.resume.startswith(COMET_PREFIX): + api = comet_ml.API() + resource = urlparse(opt.resume) + experiment_path = f"{resource.netloc}{resource.path}" + experiment = api.get(experiment_path) + set_opt_parameters(opt, experiment) + download_model_checkpoint(opt, experiment) + + return True + + return None diff --git a/yolov5/utils/loggers/comet/hpo.py b/yolov5/utils/loggers/comet/hpo.py new file mode 100644 index 0000000000000000000000000000000000000000..c225ebbd0484189762bd2f276df1096caf845195 --- /dev/null +++ b/yolov5/utils/loggers/comet/hpo.py @@ -0,0 +1,126 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +import argparse +import json +import logging +import os +import sys +from pathlib import Path + +import comet_ml + +logger = logging.getLogger(__name__) + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[3] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH + +from train import train +from utils.callbacks import Callbacks +from utils.general import increment_path +from utils.torch_utils import select_device + +# Project Configuration +config = comet_ml.config.get_config() +COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5") + + +def get_args(known=False): + """Parses command-line arguments for YOLOv5 training, supporting configuration of weights, data paths, + hyperparameters, and more. + """ + parser = argparse.ArgumentParser() + parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="initial weights path") + parser.add_argument("--cfg", type=str, default="", help="model.yaml path") + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") + parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path") + parser.add_argument("--epochs", type=int, default=300, help="total training epochs") + parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch") + parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)") + parser.add_argument("--rect", action="store_true", help="rectangular training") + parser.add_argument("--resume", nargs="?", const=True, default=False, help="resume most recent training") + parser.add_argument("--nosave", action="store_true", help="only save final checkpoint") + parser.add_argument("--noval", action="store_true", help="only validate final epoch") + parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor") + parser.add_argument("--noplots", action="store_true", help="save no plot files") + parser.add_argument("--evolve", type=int, nargs="?", const=300, help="evolve hyperparameters for x generations") + parser.add_argument("--bucket", type=str, default="", help="gsutil bucket") + parser.add_argument("--cache", type=str, nargs="?", const="ram", help='--cache images in "ram" (default) or "disk"') + parser.add_argument("--image-weights", action="store_true", help="use weighted image selection for training") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--multi-scale", action="store_true", help="vary img-size +/- 50%%") + parser.add_argument("--single-cls", action="store_true", help="train multi-class data as single-class") + parser.add_argument("--optimizer", type=str, choices=["SGD", "Adam", "AdamW"], default="SGD", help="optimizer") + parser.add_argument("--sync-bn", action="store_true", help="use SyncBatchNorm, only available in DDP mode") + parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") + parser.add_argument("--project", default=ROOT / "runs/train", help="save to project/name") + parser.add_argument("--name", default="exp", help="save to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--quad", action="store_true", help="quad dataloader") + parser.add_argument("--cos-lr", action="store_true", help="cosine LR scheduler") + parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon") + parser.add_argument("--patience", type=int, default=100, help="EarlyStopping patience (epochs without improvement)") + parser.add_argument("--freeze", nargs="+", type=int, default=[0], help="Freeze layers: backbone=10, first3=0 1 2") + parser.add_argument("--save-period", type=int, default=-1, help="Save checkpoint every x epochs (disabled if < 1)") + parser.add_argument("--seed", type=int, default=0, help="Global training seed") + parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify") + + # Weights & Biases arguments + parser.add_argument("--entity", default=None, help="W&B: Entity") + parser.add_argument("--upload_dataset", nargs="?", const=True, default=False, help='W&B: Upload data, "val" option') + parser.add_argument("--bbox_interval", type=int, default=-1, help="W&B: Set bounding-box image logging interval") + parser.add_argument("--artifact_alias", type=str, default="latest", help="W&B: Version of dataset artifact to use") + + # Comet Arguments + parser.add_argument("--comet_optimizer_config", type=str, help="Comet: Path to a Comet Optimizer Config File.") + parser.add_argument("--comet_optimizer_id", type=str, help="Comet: ID of the Comet Optimizer sweep.") + parser.add_argument("--comet_optimizer_objective", type=str, help="Comet: Set to 'minimize' or 'maximize'.") + parser.add_argument("--comet_optimizer_metric", type=str, help="Comet: Metric to Optimize.") + parser.add_argument( + "--comet_optimizer_workers", + type=int, + default=1, + help="Comet: Number of Parallel Workers to use with the Comet Optimizer.", + ) + + return parser.parse_known_args()[0] if known else parser.parse_args() + + +def run(parameters, opt): + """Executes YOLOv5 training with given hyperparameters and options, setting up device and training directories.""" + hyp_dict = {k: v for k, v in parameters.items() if k not in ["epochs", "batch_size"]} + + opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve)) + opt.batch_size = parameters.get("batch_size") + opt.epochs = parameters.get("epochs") + + device = select_device(opt.device, batch_size=opt.batch_size) + train(hyp_dict, opt, device, callbacks=Callbacks()) + + +if __name__ == "__main__": + opt = get_args(known=True) + + opt.weights = str(opt.weights) + opt.cfg = str(opt.cfg) + opt.data = str(opt.data) + opt.project = str(opt.project) + + optimizer_id = os.getenv("COMET_OPTIMIZER_ID") + if optimizer_id is None: + with open(opt.comet_optimizer_config) as f: + optimizer_config = json.load(f) + optimizer = comet_ml.Optimizer(optimizer_config) + else: + optimizer = comet_ml.Optimizer(optimizer_id) + + opt.comet_optimizer_id = optimizer.id + status = optimizer.status() + + opt.comet_optimizer_objective = status["spec"]["objective"] + opt.comet_optimizer_metric = status["spec"]["metric"] + + logger.info("COMET INFO: Starting Hyperparameter Sweep") + for parameter in optimizer.get_parameters(): + run(parameter["parameters"], opt) diff --git a/yolov5/utils/loggers/comet/optimizer_config.json b/yolov5/utils/loggers/comet/optimizer_config.json new file mode 100644 index 0000000000000000000000000000000000000000..0218f162d9d693854b961564120d46580e4614d7 --- /dev/null +++ b/yolov5/utils/loggers/comet/optimizer_config.json @@ -0,0 +1,135 @@ +{ + "algorithm": "random", + "parameters": { + "anchor_t": { + "type": "discrete", + "values": [2, 8] + }, + "batch_size": { + "type": "discrete", + "values": [16, 32, 64] + }, + "box": { + "type": "discrete", + "values": [0.02, 0.2] + }, + "cls": { + "type": "discrete", + "values": [0.2] + }, + "cls_pw": { + "type": "discrete", + "values": [0.5] + }, + "copy_paste": { + "type": "discrete", + "values": [1] + }, + "degrees": { + "type": "discrete", + "values": [0, 45] + }, + "epochs": { + "type": "discrete", + "values": [5] + }, + "fl_gamma": { + "type": "discrete", + "values": [0] + }, + "fliplr": { + "type": "discrete", + "values": [0] + }, + "flipud": { + "type": "discrete", + "values": [0] + }, + "hsv_h": { + "type": "discrete", + "values": [0] + }, + "hsv_s": { + "type": "discrete", + "values": [0] + }, + "hsv_v": { + "type": "discrete", + "values": [0] + }, + "iou_t": { + "type": "discrete", + "values": [0.7] + }, + "lr0": { + "type": "discrete", + "values": [1e-5, 0.1] + }, + "lrf": { + "type": "discrete", + "values": [0.01, 1] + }, + "mixup": { + "type": "discrete", + "values": [1] + }, + "momentum": { + "type": "discrete", + "values": [0.6] + }, + "mosaic": { + "type": "discrete", + "values": [0] + }, + "obj": { + "type": "discrete", + "values": [0.2] + }, + "obj_pw": { + "type": "discrete", + "values": [0.5] + }, + "optimizer": { + "type": "categorical", + "values": ["SGD", "Adam", "AdamW"] + }, + "perspective": { + "type": "discrete", + "values": [0] + }, + "scale": { + "type": "discrete", + "values": [0] + }, + "shear": { + "type": "discrete", + "values": [0] + }, + "translate": { + "type": "discrete", + "values": [0] + }, + "warmup_bias_lr": { + "type": "discrete", + "values": [0, 0.2] + }, + "warmup_epochs": { + "type": "discrete", + "values": [5] + }, + "warmup_momentum": { + "type": "discrete", + "values": [0, 0.95] + }, + "weight_decay": { + "type": "discrete", + "values": [0, 0.001] + } + }, + "spec": { + "maxCombo": 0, + "metric": "metrics/mAP_0.5", + "objective": "maximize" + }, + "trials": 1 +} diff --git a/yolov5/utils/loggers/wandb/__init__.py b/yolov5/utils/loggers/wandb/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/yolov5/utils/loggers/wandb/wandb_utils.py b/yolov5/utils/loggers/wandb/wandb_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..930f2c7543af0a32131b83f3bb559978d74c5fb2 --- /dev/null +++ b/yolov5/utils/loggers/wandb/wandb_utils.py @@ -0,0 +1,210 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +# WARNING ⚠️ wandb is deprecated and will be removed in future release. +# See supported integrations at https://github.com/ultralytics/yolov5#integrations + +import logging +import os +import sys +from contextlib import contextmanager +from pathlib import Path + +from utils.general import LOGGER, colorstr + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[3] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +RANK = int(os.getenv("RANK", -1)) +DEPRECATION_WARNING = ( + f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. " + f'See supported integrations at https://github.com/ultralytics/yolov5#integrations.' +) + +try: + import wandb + + assert hasattr(wandb, "__version__") # verify package import not local dir + LOGGER.warning(DEPRECATION_WARNING) +except (ImportError, AssertionError): + wandb = None + + +class WandbLogger: + """ + Log training runs, datasets, models, and predictions to Weights & Biases. + + This logger sends information to W&B at wandb.ai. By default, this information includes hyperparameters, system + configuration and metrics, model metrics, and basic data metrics and analyses. + + By providing additional command line arguments to train.py, datasets, models and predictions can also be logged. + + For more on how this logger is used, see the Weights & Biases documentation: + https://docs.wandb.com/guides/integrations/yolov5 + """ + + def __init__(self, opt, run_id=None, job_type="Training"): + """ + - Initialize WandbLogger instance + - Upload dataset if opt.upload_dataset is True + - Setup training processes if job_type is 'Training' + + arguments: + opt (namespace) -- Commandline arguments for this run + run_id (str) -- Run ID of W&B run to be resumed + job_type (str) -- To set the job_type for this run + + """ + # Pre-training routine -- + self.job_type = job_type + self.wandb, self.wandb_run = wandb, wandb.run if wandb else None + self.val_artifact, self.train_artifact = None, None + self.train_artifact_path, self.val_artifact_path = None, None + self.result_artifact = None + self.val_table, self.result_table = None, None + self.max_imgs_to_log = 16 + self.data_dict = None + if self.wandb: + self.wandb_run = wandb.run or wandb.init( + config=opt, + resume="allow", + project="YOLOv5" if opt.project == "runs/train" else Path(opt.project).stem, + entity=opt.entity, + name=opt.name if opt.name != "exp" else None, + job_type=job_type, + id=run_id, + allow_val_change=True, + ) + + if self.wandb_run and self.job_type == "Training": + if isinstance(opt.data, dict): + # This means another dataset manager has already processed the dataset info (e.g. ClearML) + # and they will have stored the already processed dict in opt.data + self.data_dict = opt.data + self.setup_training(opt) + + def setup_training(self, opt): + """ + Setup the necessary processes for training YOLO models: + - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX + - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded + - Setup log_dict, initialize bbox_interval + + arguments: + opt (namespace) -- commandline arguments for this run + + """ + self.log_dict, self.current_epoch = {}, 0 + self.bbox_interval = opt.bbox_interval + if isinstance(opt.resume, str): + model_dir, _ = self.download_model_artifact(opt) + if model_dir: + self.weights = Path(model_dir) / "last.pt" + config = self.wandb_run.config + opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = ( + str(self.weights), + config.save_period, + config.batch_size, + config.bbox_interval, + config.epochs, + config.hyp, + config.imgsz, + ) + + if opt.bbox_interval == -1: + self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 + if opt.evolve or opt.noplots: + self.bbox_interval = opt.bbox_interval = opt.epochs + 1 # disable bbox_interval + + def log_model(self, path, opt, epoch, fitness_score, best_model=False): + """ + Log the model checkpoint as W&B artifact. + + arguments: + path (Path) -- Path of directory containing the checkpoints + opt (namespace) -- Command line arguments for this run + epoch (int) -- Current epoch number + fitness_score (float) -- fitness score for current epoch + best_model (boolean) -- Boolean representing if the current checkpoint is the best yet. + """ + model_artifact = wandb.Artifact( + f"run_{wandb.run.id}_model", + type="model", + metadata={ + "original_url": str(path), + "epochs_trained": epoch + 1, + "save period": opt.save_period, + "project": opt.project, + "total_epochs": opt.epochs, + "fitness_score": fitness_score, + }, + ) + model_artifact.add_file(str(path / "last.pt"), name="last.pt") + wandb.log_artifact( + model_artifact, + aliases=[ + "latest", + "last", + f"epoch {str(self.current_epoch)}", + "best" if best_model else "", + ], + ) + LOGGER.info(f"Saving model artifact on epoch {epoch + 1}") + + def val_one_image(self, pred, predn, path, names, im): + """Evaluates model prediction for a single image, returning metrics and visualizations.""" + pass + + def log(self, log_dict): + """ + Save the metrics to the logging dictionary. + + arguments: + log_dict (Dict) -- metrics/media to be logged in current step + """ + if self.wandb_run: + for key, value in log_dict.items(): + self.log_dict[key] = value + + def end_epoch(self): + """ + Commit the log_dict, model artifacts and Tables to W&B and flush the log_dict. + + arguments: + best_result (boolean): Boolean representing if the result of this evaluation is best or not + """ + if self.wandb_run: + with all_logging_disabled(): + try: + wandb.log(self.log_dict) + except BaseException as e: + LOGGER.info( + f"An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}" + ) + self.wandb_run.finish() + self.wandb_run = None + self.log_dict = {} + + def finish_run(self): + """Log metrics if any and finish the current W&B run.""" + if self.wandb_run: + if self.log_dict: + with all_logging_disabled(): + wandb.log(self.log_dict) + wandb.run.finish() + LOGGER.warning(DEPRECATION_WARNING) + + +@contextmanager +def all_logging_disabled(highest_level=logging.CRITICAL): + """source - https://gist.github.com/simon-weber/7853144 + A context manager that will prevent any logging messages triggered during the body from being processed. + :param highest_level: the maximum logging level in use. + This would only need to be changed if a custom level greater than CRITICAL is defined. + """ + previous_level = logging.root.manager.disable + logging.disable(highest_level) + try: + yield + finally: + logging.disable(previous_level) diff --git a/yolov5/utils/loss.py b/yolov5/utils/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..e8f148e77c749d30eaf9c4e540ebc3eb7e45d01e --- /dev/null +++ b/yolov5/utils/loss.py @@ -0,0 +1,254 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Loss functions.""" + +import torch +import torch.nn as nn + +from utils.metrics import bbox_iou +from utils.torch_utils import de_parallel + + +def smooth_BCE(eps=0.1): + """Returns label smoothing BCE targets for reducing overfitting; pos: `1.0 - 0.5*eps`, neg: `0.5*eps`. For details see https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441""" + return 1.0 - 0.5 * eps, 0.5 * eps + + +class BCEBlurWithLogitsLoss(nn.Module): + # BCEwithLogitLoss() with reduced missing label effects. + def __init__(self, alpha=0.05): + """Initializes a modified BCEWithLogitsLoss with reduced missing label effects, taking optional alpha smoothing + parameter. + """ + super().__init__() + self.loss_fcn = nn.BCEWithLogitsLoss(reduction="none") # must be nn.BCEWithLogitsLoss() + self.alpha = alpha + + def forward(self, pred, true): + """Computes modified BCE loss for YOLOv5 with reduced missing label effects, taking pred and true tensors, + returns mean loss. + """ + loss = self.loss_fcn(pred, true) + pred = torch.sigmoid(pred) # prob from logits + dx = pred - true # reduce only missing label effects + # dx = (pred - true).abs() # reduce missing label and false label effects + alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4)) + loss *= alpha_factor + return loss.mean() + + +class FocalLoss(nn.Module): + # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + """Initializes FocalLoss with specified loss function, gamma, and alpha values; modifies loss reduction to + 'none'. + """ + super().__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = "none" # required to apply FL to each element + + def forward(self, pred, true): + """Calculates the focal loss between predicted and true labels using a modified BCEWithLogitsLoss.""" + loss = self.loss_fcn(pred, true) + # p_t = torch.exp(-loss) + # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability + + # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py + pred_prob = torch.sigmoid(pred) # prob from logits + p_t = true * pred_prob + (1 - true) * (1 - pred_prob) + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = (1.0 - p_t) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == "mean": + return loss.mean() + elif self.reduction == "sum": + return loss.sum() + else: # 'none' + return loss + + +class QFocalLoss(nn.Module): + # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + """Initializes Quality Focal Loss with given loss function, gamma, alpha; modifies reduction to 'none'.""" + super().__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = "none" # required to apply FL to each element + + def forward(self, pred, true): + """Computes the focal loss between `pred` and `true` using BCEWithLogitsLoss, adjusting for imbalance with + `gamma` and `alpha`. + """ + loss = self.loss_fcn(pred, true) + + pred_prob = torch.sigmoid(pred) # prob from logits + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = torch.abs(true - pred_prob) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == "mean": + return loss.mean() + elif self.reduction == "sum": + return loss.sum() + else: # 'none' + return loss + + +class ComputeLoss: + sort_obj_iou = False + + # Compute losses + def __init__(self, model, autobalance=False): + """Initializes ComputeLoss with model and autobalance option, autobalances losses if True.""" + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["cls_pw"]], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["obj_pw"]], device=device)) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE(eps=h.get("label_smoothing", 0.0)) # positive, negative BCE targets + + # Focal loss + g = h["fl_gamma"] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + m = de_parallel(model).model[-1] # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 + self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance + self.na = m.na # number of anchors + self.nc = m.nc # number of classes + self.nl = m.nl # number of layers + self.anchors = m.anchors + self.device = device + + def __call__(self, p, targets): # predictions, targets + """Performs forward pass, calculating class, box, and object loss for given predictions and targets.""" + lcls = torch.zeros(1, device=self.device) # class loss + lbox = torch.zeros(1, device=self.device) # box loss + lobj = torch.zeros(1, device=self.device) # object loss + tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = indices[i] # image, anchor, gridy, gridx + tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj + + n = b.shape[0] # number of targets + if n: + # pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1) # faster, requires torch 1.8.0 + pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1) # target-subset of predictions + + # Regression + pxy = pxy.sigmoid() * 2 - 0.5 + pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + iou = iou.detach().clamp(0).type(tobj.dtype) + if self.sort_obj_iou: + j = iou.argsort() + b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j] + if self.gr < 1: + iou = (1.0 - self.gr) + self.gr * iou + tobj[b, a, gj, gi] = iou # iou ratio + + # Classification + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(pcls, self.cn, device=self.device) # targets + t[range(n), tcls[i]] = self.cp + lcls += self.BCEcls(pcls, t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + obji = self.BCEobj(pi[..., 4], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp["box"] + lobj *= self.hyp["obj"] + lcls *= self.hyp["cls"] + bs = tobj.shape[0] # batch size + + return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach() + + def build_targets(self, p, targets): + """Prepares model targets from input targets (image,class,x,y,w,h) for loss computation, returning class, box, + indices, and anchors. + """ + na, nt = self.na, targets.shape[0] # number of anchors, targets + tcls, tbox, indices, anch = [], [], [], [] + gain = torch.ones(7, device=self.device) # normalized to gridspace gain + ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2) # append anchor indices + + g = 0.5 # bias + off = ( + torch.tensor( + [ + [0, 0], + [1, 0], + [0, 1], + [-1, 0], + [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], + device=self.device, + ).float() + * g + ) # offsets + + for i in range(self.nl): + anchors, shape = self.anchors[i], p[i].shape + gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain # shape(3,n,7) + if nt: + # Matches + r = t[..., 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1 / r).max(2)[0] < self.hyp["anchor_t"] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1 < g) & (gxy > 1)).T + l, m = ((gxi % 1 < g) & (gxi > 1)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors + a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class + gij = (gxy - offsets).long() + gi, gj = gij.T # grid indices + + # Append + indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid + tbox.append(torch.cat((gxy - gij, gwh), 1)) # box + anch.append(anchors[a]) # anchors + tcls.append(c) # class + + return tcls, tbox, indices, anch diff --git a/yolov5/utils/metrics.py b/yolov5/utils/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..385fdc471748af4fd6d599dbda5f61c2ef921412 --- /dev/null +++ b/yolov5/utils/metrics.py @@ -0,0 +1,382 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Model validation metrics.""" + +import math +import warnings +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import torch + +from utils import TryExcept, threaded + + +def fitness(x): + """Calculates fitness of a model using weighted sum of metrics P, R, mAP@0.5, mAP@0.5:0.95.""" + w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] + return (x[:, :4] * w).sum(1) + + +def smooth(y, f=0.05): + """Applies box filter smoothing to array `y` with fraction `f`, yielding a smoothed array.""" + nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd) + p = np.ones(nf // 2) # ones padding + yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded + return np.convolve(yp, np.ones(nf) / nf, mode="valid") # y-smoothed + + +def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir=".", names=(), eps=1e-16, prefix=""): + """ + Compute the average precision, given the recall and precision curves. + + Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. + # Arguments + tp: True positives (nparray, nx1 or nx10). + conf: Objectness value from 0-1 (nparray). + pred_cls: Predicted object classes (nparray). + target_cls: True object classes (nparray). + plot: Plot precision-recall curve at mAP@0.5 + save_dir: Plot save directory + # Returns + The average precision as computed in py-faster-rcnn. + """ + + # Sort by objectness + i = np.argsort(-conf) + tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] + + # Find unique classes + unique_classes, nt = np.unique(target_cls, return_counts=True) + nc = unique_classes.shape[0] # number of classes, number of detections + + # Create Precision-Recall curve and compute AP for each class + px, py = np.linspace(0, 1, 1000), [] # for plotting + ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) + for ci, c in enumerate(unique_classes): + i = pred_cls == c + n_l = nt[ci] # number of labels + n_p = i.sum() # number of predictions + if n_p == 0 or n_l == 0: + continue + + # Accumulate FPs and TPs + fpc = (1 - tp[i]).cumsum(0) + tpc = tp[i].cumsum(0) + + # Recall + recall = tpc / (n_l + eps) # recall curve + r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases + + # Precision + precision = tpc / (tpc + fpc) # precision curve + p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score + + # AP from recall-precision curve + for j in range(tp.shape[1]): + ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) + if plot and j == 0: + py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 + + # Compute F1 (harmonic mean of precision and recall) + f1 = 2 * p * r / (p + r + eps) + names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data + names = dict(enumerate(names)) # to dict + if plot: + plot_pr_curve(px, py, ap, Path(save_dir) / f"{prefix}PR_curve.png", names) + plot_mc_curve(px, f1, Path(save_dir) / f"{prefix}F1_curve.png", names, ylabel="F1") + plot_mc_curve(px, p, Path(save_dir) / f"{prefix}P_curve.png", names, ylabel="Precision") + plot_mc_curve(px, r, Path(save_dir) / f"{prefix}R_curve.png", names, ylabel="Recall") + + i = smooth(f1.mean(0), 0.1).argmax() # max F1 index + p, r, f1 = p[:, i], r[:, i], f1[:, i] + tp = (r * nt).round() # true positives + fp = (tp / (p + eps) - tp).round() # false positives + return tp, fp, p, r, f1, ap, unique_classes.astype(int) + + +def compute_ap(recall, precision): + """Compute the average precision, given the recall and precision curves + # Arguments + recall: The recall curve (list) + precision: The precision curve (list) + # Returns + Average precision, precision curve, recall curve + """ + + # Append sentinel values to beginning and end + mrec = np.concatenate(([0.0], recall, [1.0])) + mpre = np.concatenate(([1.0], precision, [0.0])) + + # Compute the precision envelope + mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) + + # Integrate area under curve + method = "interp" # methods: 'continuous', 'interp' + if method == "interp": + x = np.linspace(0, 1, 101) # 101-point interp (COCO) + ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate + else: # 'continuous' + i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve + + return ap, mpre, mrec + + +class ConfusionMatrix: + # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix + def __init__(self, nc, conf=0.25, iou_thres=0.45): + """Initializes ConfusionMatrix with given number of classes, confidence, and IoU threshold.""" + self.matrix = np.zeros((nc + 1, nc + 1)) + self.nc = nc # number of classes + self.conf = conf + self.iou_thres = iou_thres + + def process_batch(self, detections, labels): + """ + Return intersection-over-union (Jaccard index) of boxes. + + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + detections (Array[N, 6]), x1, y1, x2, y2, conf, class + labels (Array[M, 5]), class, x1, y1, x2, y2 + Returns: + None, updates confusion matrix accordingly + """ + if detections is None: + gt_classes = labels.int() + for gc in gt_classes: + self.matrix[self.nc, gc] += 1 # background FN + return + + detections = detections[detections[:, 4] > self.conf] + gt_classes = labels[:, 0].int() + detection_classes = detections[:, 5].int() + iou = box_iou(labels[:, 1:], detections[:, :4]) + + x = torch.where(iou > self.iou_thres) + if x[0].shape[0]: + matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() + if x[0].shape[0] > 1: + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 1], return_index=True)[1]] + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 0], return_index=True)[1]] + else: + matches = np.zeros((0, 3)) + + n = matches.shape[0] > 0 + m0, m1, _ = matches.transpose().astype(int) + for i, gc in enumerate(gt_classes): + j = m0 == i + if n and sum(j) == 1: + self.matrix[detection_classes[m1[j]], gc] += 1 # correct + else: + self.matrix[self.nc, gc] += 1 # true background + + if n: + for i, dc in enumerate(detection_classes): + if not any(m1 == i): + self.matrix[dc, self.nc] += 1 # predicted background + + def tp_fp(self): + """Calculates true positives (tp) and false positives (fp) excluding the background class from the confusion + matrix. + """ + tp = self.matrix.diagonal() # true positives + fp = self.matrix.sum(1) - tp # false positives + # fn = self.matrix.sum(0) - tp # false negatives (missed detections) + return tp[:-1], fp[:-1] # remove background class + + @TryExcept("WARNING ⚠️ ConfusionMatrix plot failure") + def plot(self, normalize=True, save_dir="", names=()): + """Plots confusion matrix using seaborn, optional normalization; can save plot to specified directory.""" + import seaborn as sn + + array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1e-9) if normalize else 1) # normalize columns + array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) + + fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True) + nc, nn = self.nc, len(names) # number of classes, names + sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size + labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels + ticklabels = (names + ["background"]) if labels else "auto" + with warnings.catch_warnings(): + warnings.simplefilter("ignore") # suppress empty matrix RuntimeWarning: All-NaN slice encountered + sn.heatmap( + array, + ax=ax, + annot=nc < 30, + annot_kws={"size": 8}, + cmap="Blues", + fmt=".2f", + square=True, + vmin=0.0, + xticklabels=ticklabels, + yticklabels=ticklabels, + ).set_facecolor((1, 1, 1)) + ax.set_xlabel("True") + ax.set_ylabel("Predicted") + ax.set_title("Confusion Matrix") + fig.savefig(Path(save_dir) / "confusion_matrix.png", dpi=250) + plt.close(fig) + + def print(self): + """Prints the confusion matrix row-wise, with each class and its predictions separated by spaces.""" + for i in range(self.nc + 1): + print(" ".join(map(str, self.matrix[i]))) + + +def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): + """ + Calculates IoU, GIoU, DIoU, or CIoU between two boxes, supporting xywh/xyxy formats. + + Input shapes are box1(1,4) to box2(n,4). + """ + + # Get the coordinates of bounding boxes + if xywh: # transform from xywh to xyxy + (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1) + w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2 + b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_ + b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_ + else: # x1, y1, x2, y2 = box1 + b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1) + b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1) + w1, h1 = b1_x2 - b1_x1, (b1_y2 - b1_y1).clamp(eps) + w2, h2 = b2_x2 - b2_x1, (b2_y2 - b2_y1).clamp(eps) + + # Intersection area + inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp(0) * ( + b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1) + ).clamp(0) + + # Union Area + union = w1 * h1 + w2 * h2 - inter + eps + + # IoU + iou = inter / union + if CIoU or DIoU or GIoU: + cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width + ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height + if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 + c2 = cw**2 + ch**2 + eps # convex diagonal squared + rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2 + if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 + v = (4 / math.pi**2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2) + with torch.no_grad(): + alpha = v / (v - iou + (1 + eps)) + return iou - (rho2 / c2 + v * alpha) # CIoU + return iou - rho2 / c2 # DIoU + c_area = cw * ch + eps # convex area + return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf + return iou # IoU + + +def box_iou(box1, box2, eps=1e-7): + # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py + """ + Return intersection-over-union (Jaccard index) of boxes. + + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + box1 (Tensor[N, 4]) + box2 (Tensor[M, 4]) + Returns: + iou (Tensor[N, M]): the NxM matrix containing the pairwise + IoU values for every element in boxes1 and boxes2 + """ + + # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) + (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2) + inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2) + + # IoU = inter / (area1 + area2 - inter) + return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps) + + +def bbox_ioa(box1, box2, eps=1e-7): + """ + Returns the intersection over box2 area given box1, box2. + + Boxes are x1y1x2y2 + box1: np.array of shape(4) + box2: np.array of shape(nx4) + returns: np.array of shape(n) + """ + + # Get the coordinates of bounding boxes + b1_x1, b1_y1, b1_x2, b1_y2 = box1 + b2_x1, b2_y1, b2_x2, b2_y2 = box2.T + + # Intersection area + inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * ( + np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1) + ).clip(0) + + # box2 area + box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps + + # Intersection over box2 area + return inter_area / box2_area + + +def wh_iou(wh1, wh2, eps=1e-7): + """Calculates the Intersection over Union (IoU) for two sets of widths and heights; `wh1` and `wh2` should be nx2 + and mx2 tensors. + """ + wh1 = wh1[:, None] # [N,1,2] + wh2 = wh2[None] # [1,M,2] + inter = torch.min(wh1, wh2).prod(2) # [N,M] + return inter / (wh1.prod(2) + wh2.prod(2) - inter + eps) # iou = inter / (area1 + area2 - inter) + + +# Plots ---------------------------------------------------------------------------------------------------------------- + + +@threaded +def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=()): + """Plots precision-recall curve, optionally per class, saving to `save_dir`; `px`, `py` are lists, `ap` is Nx2 + array, `names` optional. + """ + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + py = np.stack(py, axis=1) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py.T): + ax.plot(px, y, linewidth=1, label=f"{names[i]} {ap[i, 0]:.3f}") # plot(recall, precision) + else: + ax.plot(px, py, linewidth=1, color="grey") # plot(recall, precision) + + ax.plot(px, py.mean(1), linewidth=3, color="blue", label="all classes %.3f mAP@0.5" % ap[:, 0].mean()) + ax.set_xlabel("Recall") + ax.set_ylabel("Precision") + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + ax.set_title("Precision-Recall Curve") + fig.savefig(save_dir, dpi=250) + plt.close(fig) + + +@threaded +def plot_mc_curve(px, py, save_dir=Path("mc_curve.png"), names=(), xlabel="Confidence", ylabel="Metric"): + """Plots a metric-confidence curve for model predictions, supporting per-class visualization and smoothing.""" + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py): + ax.plot(px, y, linewidth=1, label=f"{names[i]}") # plot(confidence, metric) + else: + ax.plot(px, py.T, linewidth=1, color="grey") # plot(confidence, metric) + + y = smooth(py.mean(0), 0.05) + ax.plot(px, y, linewidth=3, color="blue", label=f"all classes {y.max():.2f} at {px[y.argmax()]:.3f}") + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + ax.set_title(f"{ylabel}-Confidence Curve") + fig.savefig(save_dir, dpi=250) + plt.close(fig) diff --git a/yolov5/utils/plots.py b/yolov5/utils/plots.py new file mode 100644 index 0000000000000000000000000000000000000000..9bec34a159fb7e61a09f176d1e9bcaae6064378d --- /dev/null +++ b/yolov5/utils/plots.py @@ -0,0 +1,516 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Plotting utils.""" + +import contextlib +import math +import os +from copy import copy +from pathlib import Path + +import cv2 +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sn +import torch +from PIL import Image, ImageDraw +from scipy.ndimage.filters import gaussian_filter1d +from ultralytics.utils.plotting import Annotator + +from utils import TryExcept, threaded +from utils.general import LOGGER, clip_boxes, increment_path, xywh2xyxy, xyxy2xywh +from utils.metrics import fitness + +# Settings +RANK = int(os.getenv("RANK", -1)) +matplotlib.rc("font", **{"size": 11}) +matplotlib.use("Agg") # for writing to files only + + +class Colors: + # Ultralytics color palette https://ultralytics.com/ + def __init__(self): + """ + Initializes the Colors class with a palette derived from Ultralytics color scheme, converting hex codes to RGB. + + Colors derived from `hex = matplotlib.colors.TABLEAU_COLORS.values()`. + """ + hexs = ( + "FF3838", + "FF9D97", + "FF701F", + "FFB21D", + "CFD231", + "48F90A", + "92CC17", + "3DDB86", + "1A9334", + "00D4BB", + "2C99A8", + "00C2FF", + "344593", + "6473FF", + "0018EC", + "8438FF", + "520085", + "CB38FF", + "FF95C8", + "FF37C7", + ) + self.palette = [self.hex2rgb(f"#{c}") for c in hexs] + self.n = len(self.palette) + + def __call__(self, i, bgr=False): + """Returns color from palette by index `i`, in BGR format if `bgr=True`, else RGB; `i` is an integer index.""" + c = self.palette[int(i) % self.n] + return (c[2], c[1], c[0]) if bgr else c + + @staticmethod + def hex2rgb(h): + """Converts hexadecimal color `h` to an RGB tuple (PIL-compatible) with order (R, G, B).""" + return tuple(int(h[1 + i : 1 + i + 2], 16) for i in (0, 2, 4)) + + +colors = Colors() # create instance for 'from utils.plots import colors' + + +def feature_visualization(x, module_type, stage, n=32, save_dir=Path("runs/detect/exp")): + """ + x: Features to be visualized + module_type: Module type + stage: Module stage within model + n: Maximum number of feature maps to plot + save_dir: Directory to save results + """ + if ("Detect" not in module_type) and ( + "Segment" not in module_type + ): # 'Detect' for Object Detect task,'Segment' for Segment task + batch, channels, height, width = x.shape # batch, channels, height, width + if height > 1 and width > 1: + f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename + + blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels + n = min(n, channels) # number of plots + fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols + ax = ax.ravel() + plt.subplots_adjust(wspace=0.05, hspace=0.05) + for i in range(n): + ax[i].imshow(blocks[i].squeeze()) # cmap='gray' + ax[i].axis("off") + + LOGGER.info(f"Saving {f}... ({n}/{channels})") + plt.savefig(f, dpi=300, bbox_inches="tight") + plt.close() + np.save(str(f.with_suffix(".npy")), x[0].cpu().numpy()) # npy save + + +def hist2d(x, y, n=100): + """ + Generates a logarithmic 2D histogram, useful for visualizing label or evolution distributions. + + Used in used in labels.png and evolve.png. + """ + xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n) + hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges)) + xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1) + yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1) + return np.log(hist[xidx, yidx]) + + +def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): + """Applies a low-pass Butterworth filter to `data` with specified `cutoff`, `fs`, and `order`.""" + from scipy.signal import butter, filtfilt + + # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy + def butter_lowpass(cutoff, fs, order): + """Applies a low-pass Butterworth filter to a signal with specified cutoff frequency, sample rate, and filter + order. + """ + nyq = 0.5 * fs + normal_cutoff = cutoff / nyq + return butter(order, normal_cutoff, btype="low", analog=False) + + b, a = butter_lowpass(cutoff, fs, order=order) + return filtfilt(b, a, data) # forward-backward filter + + +def output_to_target(output, max_det=300): + """Converts YOLOv5 model output to [batch_id, class_id, x, y, w, h, conf] format for plotting, limiting detections + to `max_det`. + """ + targets = [] + for i, o in enumerate(output): + box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1) + j = torch.full((conf.shape[0], 1), i) + targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1)) + return torch.cat(targets, 0).numpy() + + +@threaded +def plot_images(images, targets, paths=None, fname="images.jpg", names=None): + """Plots an image grid with labels from YOLOv5 predictions or targets, saving to `fname`.""" + if isinstance(images, torch.Tensor): + images = images.cpu().float().numpy() + if isinstance(targets, torch.Tensor): + targets = targets.cpu().numpy() + + max_size = 1920 # max image size + max_subplots = 16 # max image subplots, i.e. 4x4 + bs, _, h, w = images.shape # batch size, _, height, width + bs = min(bs, max_subplots) # limit plot images + ns = np.ceil(bs**0.5) # number of subplots (square) + if np.max(images[0]) <= 1: + images *= 255 # de-normalise (optional) + + # Build Image + mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init + for i, im in enumerate(images): + if i == max_subplots: # if last batch has fewer images than we expect + break + x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin + im = im.transpose(1, 2, 0) + mosaic[y : y + h, x : x + w, :] = im + + # Resize (optional) + scale = max_size / ns / max(h, w) + if scale < 1: + h = math.ceil(scale * h) + w = math.ceil(scale * w) + mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h))) + + # Annotate + fs = int((h + w) * ns * 0.01) # font size + annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names) + for i in range(i + 1): + x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin + annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders + if paths: + annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames + if len(targets) > 0: + ti = targets[targets[:, 0] == i] # image targets + boxes = xywh2xyxy(ti[:, 2:6]).T + classes = ti[:, 1].astype("int") + labels = ti.shape[1] == 6 # labels if no conf column + conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred) + + if boxes.shape[1]: + if boxes.max() <= 1.01: # if normalized with tolerance 0.01 + boxes[[0, 2]] *= w # scale to pixels + boxes[[1, 3]] *= h + elif scale < 1: # absolute coords need scale if image scales + boxes *= scale + boxes[[0, 2]] += x + boxes[[1, 3]] += y + for j, box in enumerate(boxes.T.tolist()): + cls = classes[j] + color = colors(cls) + cls = names[cls] if names else cls + if labels or conf[j] > 0.25: # 0.25 conf thresh + label = f"{cls}" if labels else f"{cls} {conf[j]:.1f}" + annotator.box_label(box, label, color=color) + annotator.im.save(fname) # save + + +def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=""): + """Plots learning rate schedule for given optimizer and scheduler, saving plot to `save_dir`.""" + optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals + y = [] + for _ in range(epochs): + scheduler.step() + y.append(optimizer.param_groups[0]["lr"]) + plt.plot(y, ".-", label="LR") + plt.xlabel("epoch") + plt.ylabel("LR") + plt.grid() + plt.xlim(0, epochs) + plt.ylim(0) + plt.savefig(Path(save_dir) / "LR.png", dpi=200) + plt.close() + + +def plot_val_txt(): + """ + Plots 2D and 1D histograms of bounding box centers from 'val.txt' using matplotlib, saving as 'hist2d.png' and + 'hist1d.png'. + + Example: from utils.plots import *; plot_val() + """ + x = np.loadtxt("val.txt", dtype=np.float32) + box = xyxy2xywh(x[:, :4]) + cx, cy = box[:, 0], box[:, 1] + + fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True) + ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0) + ax.set_aspect("equal") + plt.savefig("hist2d.png", dpi=300) + + fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True) + ax[0].hist(cx, bins=600) + ax[1].hist(cy, bins=600) + plt.savefig("hist1d.png", dpi=200) + + +def plot_targets_txt(): + """ + Plots histograms of object detection targets from 'targets.txt', saving the figure as 'targets.jpg'. + + Example: from utils.plots import *; plot_targets_txt() + """ + x = np.loadtxt("targets.txt", dtype=np.float32).T + s = ["x targets", "y targets", "width targets", "height targets"] + fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) + ax = ax.ravel() + for i in range(4): + ax[i].hist(x[i], bins=100, label=f"{x[i].mean():.3g} +/- {x[i].std():.3g}") + ax[i].legend() + ax[i].set_title(s[i]) + plt.savefig("targets.jpg", dpi=200) + + +def plot_val_study(file="", dir="", x=None): + """ + Plots validation study results from 'study*.txt' files in a directory or a specific file, comparing model + performance and speed. + + Example: from utils.plots import *; plot_val_study() + """ + save_dir = Path(file).parent if file else Path(dir) + plot2 = False # plot additional results + if plot2: + ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel() + + fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) + # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]: + for f in sorted(save_dir.glob("study*.txt")): + y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T + x = np.arange(y.shape[1]) if x is None else np.array(x) + if plot2: + s = ["P", "R", "mAP@.5", "mAP@.5:.95", "t_preprocess (ms/img)", "t_inference (ms/img)", "t_NMS (ms/img)"] + for i in range(7): + ax[i].plot(x, y[i], ".-", linewidth=2, markersize=8) + ax[i].set_title(s[i]) + + j = y[3].argmax() + 1 + ax2.plot( + y[5, 1:j], + y[3, 1:j] * 1e2, + ".-", + linewidth=2, + markersize=8, + label=f.stem.replace("study_coco_", "").replace("yolo", "YOLO"), + ) + + ax2.plot( + 1e3 / np.array([209, 140, 97, 58, 35, 18]), + [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], + "k.-", + linewidth=2, + markersize=8, + alpha=0.25, + label="EfficientDet", + ) + + ax2.grid(alpha=0.2) + ax2.set_yticks(np.arange(20, 60, 5)) + ax2.set_xlim(0, 57) + ax2.set_ylim(25, 55) + ax2.set_xlabel("GPU Speed (ms/img)") + ax2.set_ylabel("COCO AP val") + ax2.legend(loc="lower right") + f = save_dir / "study.png" + print(f"Saving {f}...") + plt.savefig(f, dpi=300) + + +@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395 +def plot_labels(labels, names=(), save_dir=Path("")): + """Plots dataset labels, saving correlogram and label images, handles classes, and visualizes bounding boxes.""" + LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ") + c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes + nc = int(c.max() + 1) # number of classes + x = pd.DataFrame(b.transpose(), columns=["x", "y", "width", "height"]) + + # seaborn correlogram + sn.pairplot(x, corner=True, diag_kind="auto", kind="hist", diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9)) + plt.savefig(save_dir / "labels_correlogram.jpg", dpi=200) + plt.close() + + # matplotlib labels + matplotlib.use("svg") # faster + ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() + y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) + with contextlib.suppress(Exception): # color histogram bars by class + [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195 + ax[0].set_ylabel("instances") + if 0 < len(names) < 30: + ax[0].set_xticks(range(len(names))) + ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10) + else: + ax[0].set_xlabel("classes") + sn.histplot(x, x="x", y="y", ax=ax[2], bins=50, pmax=0.9) + sn.histplot(x, x="width", y="height", ax=ax[3], bins=50, pmax=0.9) + + # rectangles + labels[:, 1:3] = 0.5 # center + labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000 + img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255) + for cls, *box in labels[:1000]: + ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot + ax[1].imshow(img) + ax[1].axis("off") + + for a in [0, 1, 2, 3]: + for s in ["top", "right", "left", "bottom"]: + ax[a].spines[s].set_visible(False) + + plt.savefig(save_dir / "labels.jpg", dpi=200) + matplotlib.use("Agg") + plt.close() + + +def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path("images.jpg")): + """Displays a grid of images with optional labels and predictions, saving to a file.""" + from utils.augmentations import denormalize + + names = names or [f"class{i}" for i in range(1000)] + blocks = torch.chunk( + denormalize(im.clone()).cpu().float(), len(im), dim=0 + ) # select batch index 0, block by channels + n = min(len(blocks), nmax) # number of plots + m = min(8, round(n**0.5)) # 8 x 8 default + fig, ax = plt.subplots(math.ceil(n / m), m) # 8 rows x n/8 cols + ax = ax.ravel() if m > 1 else [ax] + # plt.subplots_adjust(wspace=0.05, hspace=0.05) + for i in range(n): + ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0)) + ax[i].axis("off") + if labels is not None: + s = names[labels[i]] + (f"—{names[pred[i]]}" if pred is not None else "") + ax[i].set_title(s, fontsize=8, verticalalignment="top") + plt.savefig(f, dpi=300, bbox_inches="tight") + plt.close() + if verbose: + LOGGER.info(f"Saving {f}") + if labels is not None: + LOGGER.info("True: " + " ".join(f"{names[i]:3s}" for i in labels[:nmax])) + if pred is not None: + LOGGER.info("Predicted:" + " ".join(f"{names[i]:3s}" for i in pred[:nmax])) + return f + + +def plot_evolve(evolve_csv="path/to/evolve.csv"): + """ + Plots hyperparameter evolution results from a given CSV, saving the plot and displaying best results. + + Example: from utils.plots import *; plot_evolve() + """ + evolve_csv = Path(evolve_csv) + data = pd.read_csv(evolve_csv) + keys = [x.strip() for x in data.columns] + x = data.values + f = fitness(x) + j = np.argmax(f) # max fitness index + plt.figure(figsize=(10, 12), tight_layout=True) + matplotlib.rc("font", **{"size": 8}) + print(f"Best results from row {j} of {evolve_csv}:") + for i, k in enumerate(keys[7:]): + v = x[:, 7 + i] + mu = v[j] # best single result + plt.subplot(6, 5, i + 1) + plt.scatter(v, f, c=hist2d(v, f, 20), cmap="viridis", alpha=0.8, edgecolors="none") + plt.plot(mu, f.max(), "k+", markersize=15) + plt.title(f"{k} = {mu:.3g}", fontdict={"size": 9}) # limit to 40 characters + if i % 5 != 0: + plt.yticks([]) + print(f"{k:>15}: {mu:.3g}") + f = evolve_csv.with_suffix(".png") # filename + plt.savefig(f, dpi=200) + plt.close() + print(f"Saved {f}") + + +def plot_results(file="path/to/results.csv", dir=""): + """ + Plots training results from a 'results.csv' file; accepts file path and directory as arguments. + + Example: from utils.plots import *; plot_results('path/to/results.csv') + """ + save_dir = Path(file).parent if file else Path(dir) + fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True) + ax = ax.ravel() + files = list(save_dir.glob("results*.csv")) + assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot." + for f in files: + try: + data = pd.read_csv(f) + s = [x.strip() for x in data.columns] + x = data.values[:, 0] + for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]): + y = data.values[:, j].astype("float") + # y[y == 0] = np.nan # don't show zero values + ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=8) # actual results + ax[i].plot(x, gaussian_filter1d(y, sigma=3), ":", label="smooth", linewidth=2) # smoothing line + ax[i].set_title(s[j], fontsize=12) + # if j in [8, 9, 10]: # share train and val loss y axes + # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) + except Exception as e: + LOGGER.info(f"Warning: Plotting error for {f}: {e}") + ax[1].legend() + fig.savefig(save_dir / "results.png", dpi=200) + plt.close() + + +def profile_idetection(start=0, stop=0, labels=(), save_dir=""): + """ + Plots per-image iDetection logs, comparing metrics like storage and performance over time. + + Example: from utils.plots import *; profile_idetection() + """ + ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel() + s = ["Images", "Free Storage (GB)", "RAM Usage (GB)", "Battery", "dt_raw (ms)", "dt_smooth (ms)", "real-world FPS"] + files = list(Path(save_dir).glob("frames*.txt")) + for fi, f in enumerate(files): + try: + results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows + n = results.shape[1] # number of rows + x = np.arange(start, min(stop, n) if stop else n) + results = results[:, x] + t = results[0] - results[0].min() # set t0=0s + results[0] = x + for i, a in enumerate(ax): + if i < len(results): + label = labels[fi] if len(labels) else f.stem.replace("frames_", "") + a.plot(t, results[i], marker=".", label=label, linewidth=1, markersize=5) + a.set_title(s[i]) + a.set_xlabel("time (s)") + # if fi == len(files) - 1: + # a.set_ylim(bottom=0) + for side in ["top", "right"]: + a.spines[side].set_visible(False) + else: + a.remove() + except Exception as e: + print(f"Warning: Plotting error for {f}; {e}") + ax[1].legend() + plt.savefig(Path(save_dir) / "idetection_profile.png", dpi=200) + + +def save_one_box(xyxy, im, file=Path("im.jpg"), gain=1.02, pad=10, square=False, BGR=False, save=True): + """Crops and saves an image from bounding box `xyxy`, applied with `gain` and `pad`, optionally squares and adjusts + for BGR. + """ + xyxy = torch.tensor(xyxy).view(-1, 4) + b = xyxy2xywh(xyxy) # boxes + if square: + b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square + b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad + xyxy = xywh2xyxy(b).long() + clip_boxes(xyxy, im.shape) + crop = im[int(xyxy[0, 1]) : int(xyxy[0, 3]), int(xyxy[0, 0]) : int(xyxy[0, 2]), :: (1 if BGR else -1)] + if save: + file.parent.mkdir(parents=True, exist_ok=True) # make directory + f = str(increment_path(file).with_suffix(".jpg")) + # cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue + Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB + return crop diff --git a/yolov5/utils/segment/__init__.py b/yolov5/utils/segment/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/yolov5/utils/segment/augmentations.py b/yolov5/utils/segment/augmentations.py new file mode 100644 index 0000000000000000000000000000000000000000..d7dd8aec66919d127f90c6daa30f22412834fab5 --- /dev/null +++ b/yolov5/utils/segment/augmentations.py @@ -0,0 +1,101 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Image augmentation functions.""" + +import math +import random + +import cv2 +import numpy as np + +from ..augmentations import box_candidates +from ..general import resample_segments, segment2box + + +def mixup(im, labels, segments, im2, labels2, segments2): + """ + Applies MixUp augmentation blending two images, labels, and segments with a random ratio. + + See https://arxiv.org/pdf/1710.09412.pdf + """ + r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 + im = (im * r + im2 * (1 - r)).astype(np.uint8) + labels = np.concatenate((labels, labels2), 0) + segments = np.concatenate((segments, segments2), 0) + return im, labels, segments + + +def random_perspective( + im, targets=(), segments=(), degrees=10, translate=0.1, scale=0.1, shear=10, perspective=0.0, border=(0, 0) +): + # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) + # targets = [cls, xyxy] + + """Applies random perspective, rotation, scale, shear, and translation augmentations to an image and targets.""" + height = im.shape[0] + border[0] * 2 # shape(h,w,c) + width = im.shape[1] + border[1] * 2 + + # Center + C = np.eye(3) + C[0, 2] = -im.shape[1] / 2 # x translation (pixels) + C[1, 2] = -im.shape[0] / 2 # y translation (pixels) + + # Perspective + P = np.eye(3) + P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) + P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) + + # Rotation and Scale + R = np.eye(3) + a = random.uniform(-degrees, degrees) + # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations + s = random.uniform(1 - scale, 1 + scale) + # s = 2 ** random.uniform(-scale, scale) + R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) + + # Shear + S = np.eye(3) + S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) + S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) + + # Translation + T = np.eye(3) + T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) + T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) + + # Combined rotation matrix + M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT + if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed + if perspective: + im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) + else: # affine + im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) + + # Visualize + # import matplotlib.pyplot as plt + # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() + # ax[0].imshow(im[:, :, ::-1]) # base + # ax[1].imshow(im2[:, :, ::-1]) # warped + + # Transform label coordinates + n = len(targets) + new_segments = [] + if n: + new = np.zeros((n, 4)) + segments = resample_segments(segments) # upsample + for i, segment in enumerate(segments): + xy = np.ones((len(segment), 3)) + xy[:, :2] = segment + xy = xy @ M.T # transform + xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine + + # clip + new[i] = segment2box(xy, width, height) + new_segments.append(xy) + + # filter candidates + i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01) + targets = targets[i] + targets[:, 1:5] = new[i] + new_segments = np.array(new_segments)[i] + + return im, targets, new_segments diff --git a/yolov5/utils/segment/dataloaders.py b/yolov5/utils/segment/dataloaders.py new file mode 100644 index 0000000000000000000000000000000000000000..c2be5f0dfe9a9f8eb4ebb663ba804f840c96524f --- /dev/null +++ b/yolov5/utils/segment/dataloaders.py @@ -0,0 +1,364 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Dataloaders.""" + +import os +import random + +import cv2 +import numpy as np +import torch +from torch.utils.data import DataLoader + +from ..augmentations import augment_hsv, copy_paste, letterbox +from ..dataloaders import InfiniteDataLoader, LoadImagesAndLabels, SmartDistributedSampler, seed_worker +from ..general import LOGGER, xyn2xy, xywhn2xyxy, xyxy2xywhn +from ..torch_utils import torch_distributed_zero_first +from .augmentations import mixup, random_perspective + +RANK = int(os.getenv("RANK", -1)) + + +def create_dataloader( + path, + imgsz, + batch_size, + stride, + single_cls=False, + hyp=None, + augment=False, + cache=False, + pad=0.0, + rect=False, + rank=-1, + workers=8, + image_weights=False, + quad=False, + prefix="", + shuffle=False, + mask_downsample_ratio=1, + overlap_mask=False, + seed=0, +): + """Creates a dataloader for training, validating, or testing YOLO models with various dataset options.""" + if rect and shuffle: + LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False") + shuffle = False + with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP + dataset = LoadImagesAndLabelsAndMasks( + path, + imgsz, + batch_size, + augment=augment, # augmentation + hyp=hyp, # hyperparameters + rect=rect, # rectangular batches + cache_images=cache, + single_cls=single_cls, + stride=int(stride), + pad=pad, + image_weights=image_weights, + prefix=prefix, + downsample_ratio=mask_downsample_ratio, + overlap=overlap_mask, + rank=rank, + ) + + batch_size = min(batch_size, len(dataset)) + nd = torch.cuda.device_count() # number of CUDA devices + nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers + sampler = None if rank == -1 else SmartDistributedSampler(dataset, shuffle=shuffle) + loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates + generator = torch.Generator() + generator.manual_seed(6148914691236517205 + seed + RANK) + return loader( + dataset, + batch_size=batch_size, + shuffle=shuffle and sampler is None, + num_workers=nw, + sampler=sampler, + pin_memory=True, + collate_fn=LoadImagesAndLabelsAndMasks.collate_fn4 if quad else LoadImagesAndLabelsAndMasks.collate_fn, + worker_init_fn=seed_worker, + generator=generator, + ), dataset + + +class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels): # for training/testing + def __init__( + self, + path, + img_size=640, + batch_size=16, + augment=False, + hyp=None, + rect=False, + image_weights=False, + cache_images=False, + single_cls=False, + stride=32, + pad=0, + min_items=0, + prefix="", + downsample_ratio=1, + overlap=False, + rank=-1, + seed=0, + ): + """Initializes the dataset with image, label, and mask loading capabilities for training/testing.""" + super().__init__( + path, + img_size, + batch_size, + augment, + hyp, + rect, + image_weights, + cache_images, + single_cls, + stride, + pad, + min_items, + prefix, + rank, + seed, + ) + self.downsample_ratio = downsample_ratio + self.overlap = overlap + + def __getitem__(self, index): + """Returns a transformed item from the dataset at the specified index, handling indexing and image weighting.""" + index = self.indices[index] # linear, shuffled, or image_weights + + hyp = self.hyp + mosaic = self.mosaic and random.random() < hyp["mosaic"] + masks = [] + if mosaic: + # Load mosaic + img, labels, segments = self.load_mosaic(index) + shapes = None + + # MixUp augmentation + if random.random() < hyp["mixup"]: + img, labels, segments = mixup(img, labels, segments, *self.load_mosaic(random.randint(0, self.n - 1))) + + else: + # Load image + img, (h0, w0), (h, w) = self.load_image(index) + + # Letterbox + shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape + img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) + shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling + + labels = self.labels[index].copy() + # [array, array, ....], array.shape=(num_points, 2), xyxyxyxy + segments = self.segments[index].copy() + if len(segments): + for i_s in range(len(segments)): + segments[i_s] = xyn2xy( + segments[i_s], + ratio[0] * w, + ratio[1] * h, + padw=pad[0], + padh=pad[1], + ) + if labels.size: # normalized xywh to pixel xyxy format + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) + + if self.augment: + img, labels, segments = random_perspective( + img, + labels, + segments=segments, + degrees=hyp["degrees"], + translate=hyp["translate"], + scale=hyp["scale"], + shear=hyp["shear"], + perspective=hyp["perspective"], + ) + + nl = len(labels) # number of labels + if nl: + labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3) + if self.overlap: + masks, sorted_idx = polygons2masks_overlap( + img.shape[:2], segments, downsample_ratio=self.downsample_ratio + ) + masks = masks[None] # (640, 640) -> (1, 640, 640) + labels = labels[sorted_idx] + else: + masks = polygons2masks(img.shape[:2], segments, color=1, downsample_ratio=self.downsample_ratio) + + masks = ( + torch.from_numpy(masks) + if len(masks) + else torch.zeros( + 1 if self.overlap else nl, img.shape[0] // self.downsample_ratio, img.shape[1] // self.downsample_ratio + ) + ) + # TODO: albumentations support + if self.augment: + # Albumentations + # there are some augmentation that won't change boxes and masks, + # so just be it for now. + img, labels = self.albumentations(img, labels) + nl = len(labels) # update after albumentations + + # HSV color-space + augment_hsv(img, hgain=hyp["hsv_h"], sgain=hyp["hsv_s"], vgain=hyp["hsv_v"]) + + # Flip up-down + if random.random() < hyp["flipud"]: + img = np.flipud(img) + if nl: + labels[:, 2] = 1 - labels[:, 2] + masks = torch.flip(masks, dims=[1]) + + # Flip left-right + if random.random() < hyp["fliplr"]: + img = np.fliplr(img) + if nl: + labels[:, 1] = 1 - labels[:, 1] + masks = torch.flip(masks, dims=[2]) + + # Cutouts # labels = cutout(img, labels, p=0.5) + + labels_out = torch.zeros((nl, 6)) + if nl: + labels_out[:, 1:] = torch.from_numpy(labels) + + # Convert + img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB + img = np.ascontiguousarray(img) + + return (torch.from_numpy(img), labels_out, self.im_files[index], shapes, masks) + + def load_mosaic(self, index): + """Loads 1 image + 3 random images into a 4-image YOLOv5 mosaic, adjusting labels and segments accordingly.""" + labels4, segments4 = [], [] + s = self.img_size + yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y + + # 3 additional image indices + indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = self.load_image(index) + + # place img in img4 + if i == 0: # top left + img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) + x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) + elif i == 1: # top right + x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc + x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h + elif i == 2: # bottom left + x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) + x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) + elif i == 3: # bottom right + x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) + x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) + + img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + padw = x1a - x1b + padh = y1a - y1b + + labels, segments = self.labels[index].copy(), self.segments[index].copy() + + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padw, padh) for x in segments] + labels4.append(labels) + segments4.extend(segments) + + # Concat/clip labels + labels4 = np.concatenate(labels4, 0) + for x in (labels4[:, 1:], *segments4): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img4, labels4 = replicate(img4, labels4) # replicate + + # Augment + img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp["copy_paste"]) + img4, labels4, segments4 = random_perspective( + img4, + labels4, + segments4, + degrees=self.hyp["degrees"], + translate=self.hyp["translate"], + scale=self.hyp["scale"], + shear=self.hyp["shear"], + perspective=self.hyp["perspective"], + border=self.mosaic_border, + ) # border to remove + return img4, labels4, segments4 + + @staticmethod + def collate_fn(batch): + """Custom collation function for DataLoader, batches images, labels, paths, shapes, and segmentation masks.""" + img, label, path, shapes, masks = zip(*batch) # transposed + batched_masks = torch.cat(masks, 0) + for i, l in enumerate(label): + l[:, 0] = i # add target image index for build_targets() + return torch.stack(img, 0), torch.cat(label, 0), path, shapes, batched_masks + + +def polygon2mask(img_size, polygons, color=1, downsample_ratio=1): + """ + Args: + img_size (tuple): The image size. + polygons (np.ndarray): [N, M], N is the number of polygons, + M is the number of points(Be divided by 2). + """ + mask = np.zeros(img_size, dtype=np.uint8) + polygons = np.asarray(polygons) + polygons = polygons.astype(np.int32) + shape = polygons.shape + polygons = polygons.reshape(shape[0], -1, 2) + cv2.fillPoly(mask, polygons, color=color) + nh, nw = (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio) + # NOTE: fillPoly firstly then resize is trying the keep the same way + # of loss calculation when mask-ratio=1. + mask = cv2.resize(mask, (nw, nh)) + return mask + + +def polygons2masks(img_size, polygons, color, downsample_ratio=1): + """ + Args: + img_size (tuple): The image size. + polygons (list[np.ndarray]): each polygon is [N, M], + N is the number of polygons, + M is the number of points(Be divided by 2). + """ + masks = [] + for si in range(len(polygons)): + mask = polygon2mask(img_size, [polygons[si].reshape(-1)], color, downsample_ratio) + masks.append(mask) + return np.array(masks) + + +def polygons2masks_overlap(img_size, segments, downsample_ratio=1): + """Return a (640, 640) overlap mask.""" + masks = np.zeros( + (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio), + dtype=np.int32 if len(segments) > 255 else np.uint8, + ) + areas = [] + ms = [] + for si in range(len(segments)): + mask = polygon2mask( + img_size, + [segments[si].reshape(-1)], + downsample_ratio=downsample_ratio, + color=1, + ) + ms.append(mask) + areas.append(mask.sum()) + areas = np.asarray(areas) + index = np.argsort(-areas) + ms = np.array(ms)[index] + for i in range(len(segments)): + mask = ms[i] * (i + 1) + masks = masks + mask + masks = np.clip(masks, a_min=0, a_max=i + 1) + return masks, index diff --git a/yolov5/utils/segment/general.py b/yolov5/utils/segment/general.py new file mode 100644 index 0000000000000000000000000000000000000000..2f65d60238ddc3ff7607a552dece52b7bff305fa --- /dev/null +++ b/yolov5/utils/segment/general.py @@ -0,0 +1,163 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F + + +def crop_mask(masks, boxes): + """ + "Crop" predicted masks by zeroing out everything not in the predicted bbox. Vectorized by Chong (thanks Chong). + + Args: + - masks should be a size [n, h, w] tensor of masks + - boxes should be a size [n, 4] tensor of bbox coords in relative point form + """ + + n, h, w = masks.shape + x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) # x1 shape(1,1,n) + r = torch.arange(w, device=masks.device, dtype=x1.dtype)[None, None, :] # rows shape(1,w,1) + c = torch.arange(h, device=masks.device, dtype=x1.dtype)[None, :, None] # cols shape(h,1,1) + + return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2)) + + +def process_mask_upsample(protos, masks_in, bboxes, shape): + """ + Crop after upsample. + protos: [mask_dim, mask_h, mask_w] + masks_in: [n, mask_dim], n is number of masks after nms + bboxes: [n, 4], n is number of masks after nms + shape: input_image_size, (h, w) + + return: h, w, n + """ + + c, mh, mw = protos.shape # CHW + masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) + masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW + masks = crop_mask(masks, bboxes) # CHW + return masks.gt_(0.5) + + +def process_mask(protos, masks_in, bboxes, shape, upsample=False): + """ + Crop before upsample. + proto_out: [mask_dim, mask_h, mask_w] + out_masks: [n, mask_dim], n is number of masks after nms + bboxes: [n, 4], n is number of masks after nms + shape:input_image_size, (h, w) + + return: h, w, n + """ + + c, mh, mw = protos.shape # CHW + ih, iw = shape + masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW + + downsampled_bboxes = bboxes.clone() + downsampled_bboxes[:, 0] *= mw / iw + downsampled_bboxes[:, 2] *= mw / iw + downsampled_bboxes[:, 3] *= mh / ih + downsampled_bboxes[:, 1] *= mh / ih + + masks = crop_mask(masks, downsampled_bboxes) # CHW + if upsample: + masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW + return masks.gt_(0.5) + + +def process_mask_native(protos, masks_in, bboxes, shape): + """ + Crop after upsample. + protos: [mask_dim, mask_h, mask_w] + masks_in: [n, mask_dim], n is number of masks after nms + bboxes: [n, 4], n is number of masks after nms + shape: input_image_size, (h, w) + + return: h, w, n + """ + c, mh, mw = protos.shape # CHW + masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) + gain = min(mh / shape[0], mw / shape[1]) # gain = old / new + pad = (mw - shape[1] * gain) / 2, (mh - shape[0] * gain) / 2 # wh padding + top, left = int(pad[1]), int(pad[0]) # y, x + bottom, right = int(mh - pad[1]), int(mw - pad[0]) + masks = masks[:, top:bottom, left:right] + + masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW + masks = crop_mask(masks, bboxes) # CHW + return masks.gt_(0.5) + + +def scale_image(im1_shape, masks, im0_shape, ratio_pad=None): + """ + img1_shape: model input shape, [h, w] + img0_shape: origin pic shape, [h, w, 3] + masks: [h, w, num] + """ + # Rescale coordinates (xyxy) from im1_shape to im0_shape + if ratio_pad is None: # calculate from im0_shape + gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new + pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding + else: + pad = ratio_pad[1] + top, left = int(pad[1]), int(pad[0]) # y, x + bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0]) + + if len(masks.shape) < 2: + raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}') + masks = masks[top:bottom, left:right] + # masks = masks.permute(2, 0, 1).contiguous() + # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0] + # masks = masks.permute(1, 2, 0).contiguous() + masks = cv2.resize(masks, (im0_shape[1], im0_shape[0])) + + if len(masks.shape) == 2: + masks = masks[:, :, None] + return masks + + +def mask_iou(mask1, mask2, eps=1e-7): + """ + mask1: [N, n] m1 means number of predicted objects + mask2: [M, n] m2 means number of gt objects + Note: n means image_w x image_h + + return: masks iou, [N, M] + """ + intersection = torch.matmul(mask1, mask2.t()).clamp(0) + union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection + return intersection / (union + eps) + + +def masks_iou(mask1, mask2, eps=1e-7): + """ + mask1: [N, n] m1 means number of predicted objects + mask2: [N, n] m2 means number of gt objects + Note: n means image_w x image_h + + return: masks iou, (N, ) + """ + intersection = (mask1 * mask2).sum(1).clamp(0) # (N, ) + union = (mask1.sum(1) + mask2.sum(1))[None] - intersection # (area1 + area2) - intersection + return intersection / (union + eps) + + +def masks2segments(masks, strategy="largest"): + """Converts binary (n,160,160) masks to polygon segments with options for concatenation or selecting the largest + segment. + """ + segments = [] + for x in masks.int().cpu().numpy().astype("uint8"): + c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] + if c: + if strategy == "concat": # concatenate all segments + c = np.concatenate([x.reshape(-1, 2) for x in c]) + elif strategy == "largest": # select largest segment + c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2) + else: + c = np.zeros((0, 2)) # no segments found + segments.append(c.astype("float32")) + return segments diff --git a/yolov5/utils/segment/loss.py b/yolov5/utils/segment/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..d4bc9d3aed54b235499e91ded8c53c6c815444b3 --- /dev/null +++ b/yolov5/utils/segment/loss.py @@ -0,0 +1,197 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..general import xywh2xyxy +from ..loss import FocalLoss, smooth_BCE +from ..metrics import bbox_iou +from ..torch_utils import de_parallel +from .general import crop_mask + + +class ComputeLoss: + # Compute losses + def __init__(self, model, autobalance=False, overlap=False): + """Initializes the compute loss function for YOLOv5 models with options for autobalancing and overlap + handling. + """ + self.sort_obj_iou = False + self.overlap = overlap + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["cls_pw"]], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["obj_pw"]], device=device)) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE(eps=h.get("label_smoothing", 0.0)) # positive, negative BCE targets + + # Focal loss + g = h["fl_gamma"] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + m = de_parallel(model).model[-1] # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 + self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance + self.na = m.na # number of anchors + self.nc = m.nc # number of classes + self.nl = m.nl # number of layers + self.nm = m.nm # number of masks + self.anchors = m.anchors + self.device = device + + def __call__(self, preds, targets, masks): # predictions, targets, model + """Evaluates YOLOv5 model's loss for given predictions, targets, and masks; returns total loss components.""" + p, proto = preds + bs, nm, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width + lcls = torch.zeros(1, device=self.device) + lbox = torch.zeros(1, device=self.device) + lobj = torch.zeros(1, device=self.device) + lseg = torch.zeros(1, device=self.device) + tcls, tbox, indices, anchors, tidxs, xywhn = self.build_targets(p, targets) # targets + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = indices[i] # image, anchor, gridy, gridx + tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj + + n = b.shape[0] # number of targets + if n: + pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, self.nc, nm), 1) # subset of predictions + + # Box regression + pxy = pxy.sigmoid() * 2 - 0.5 + pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + iou = iou.detach().clamp(0).type(tobj.dtype) + if self.sort_obj_iou: + j = iou.argsort() + b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j] + if self.gr < 1: + iou = (1.0 - self.gr) + self.gr * iou + tobj[b, a, gj, gi] = iou # iou ratio + + # Classification + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(pcls, self.cn, device=self.device) # targets + t[range(n), tcls[i]] = self.cp + lcls += self.BCEcls(pcls, t) # BCE + + # Mask regression + if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample + masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0] + marea = xywhn[i][:, 2:].prod(1) # mask width, height normalized + mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)) + for bi in b.unique(): + j = b == bi # matching index + if self.overlap: + mask_gti = torch.where(masks[bi][None] == tidxs[i][j].view(-1, 1, 1), 1.0, 0.0) + else: + mask_gti = masks[tidxs[i]][j] + lseg += self.single_mask_loss(mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j]) + + obji = self.BCEobj(pi[..., 4], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp["box"] + lobj *= self.hyp["obj"] + lcls *= self.hyp["cls"] + lseg *= self.hyp["box"] / bs + + loss = lbox + lobj + lcls + lseg + return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach() + + def single_mask_loss(self, gt_mask, pred, proto, xyxy, area): + """Calculates and normalizes single mask loss for YOLOv5 between predicted and ground truth masks.""" + pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n,32) @ (32,80,80) -> (n,80,80) + loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none") + return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean() + + def build_targets(self, p, targets): + """Prepares YOLOv5 targets for loss computation; inputs targets (image, class, x, y, w, h), output target + classes/boxes. + """ + na, nt = self.na, targets.shape[0] # number of anchors, targets + tcls, tbox, indices, anch, tidxs, xywhn = [], [], [], [], [], [] + gain = torch.ones(8, device=self.device) # normalized to gridspace gain + ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + if self.overlap: + batch = p[0].shape[0] + ti = [] + for i in range(batch): + num = (targets[:, 0] == i).sum() # find number of targets of each image + ti.append(torch.arange(num, device=self.device).float().view(1, num).repeat(na, 1) + 1) # (na, num) + ti = torch.cat(ti, 1) # (na, nt) + else: + ti = torch.arange(nt, device=self.device).float().view(1, nt).repeat(na, 1) + targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., None]), 2) # append anchor indices + + g = 0.5 # bias + off = ( + torch.tensor( + [ + [0, 0], + [1, 0], + [0, 1], + [-1, 0], + [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], + device=self.device, + ).float() + * g + ) # offsets + + for i in range(self.nl): + anchors, shape = self.anchors[i], p[i].shape + gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain # shape(3,n,7) + if nt: + # Matches + r = t[..., 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1 / r).max(2)[0] < self.hyp["anchor_t"] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1 < g) & (gxy > 1)).T + l, m = ((gxi % 1 < g) & (gxi > 1)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + bc, gxy, gwh, at = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors + (a, tidx), (b, c) = at.long().T, bc.long().T # anchors, image, class + gij = (gxy - offsets).long() + gi, gj = gij.T # grid indices + + # Append + indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid + tbox.append(torch.cat((gxy - gij, gwh), 1)) # box + anch.append(anchors[a]) # anchors + tcls.append(c) # class + tidxs.append(tidx) + xywhn.append(torch.cat((gxy, gwh), 1) / gain[2:6]) # xywh normalized + + return tcls, tbox, indices, anch, tidxs, xywhn diff --git a/yolov5/utils/segment/metrics.py b/yolov5/utils/segment/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..6f57dec132e281d8cda3821ebe45331fb3a8e537 --- /dev/null +++ b/yolov5/utils/segment/metrics.py @@ -0,0 +1,223 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Model validation metrics.""" + +import numpy as np + +from ..metrics import ap_per_class + + +def fitness(x): + """Evaluates model fitness by a weighted sum of 8 metrics, `x`: [N,8] array, weights: [0.1, 0.9] for mAP and F1.""" + w = [0.0, 0.0, 0.1, 0.9, 0.0, 0.0, 0.1, 0.9] + return (x[:, :8] * w).sum(1) + + +def ap_per_class_box_and_mask( + tp_m, + tp_b, + conf, + pred_cls, + target_cls, + plot=False, + save_dir=".", + names=(), +): + """ + Args: + tp_b: tp of boxes. + tp_m: tp of masks. + other arguments see `func: ap_per_class`. + """ + results_boxes = ap_per_class( + tp_b, conf, pred_cls, target_cls, plot=plot, save_dir=save_dir, names=names, prefix="Box" + )[2:] + results_masks = ap_per_class( + tp_m, conf, pred_cls, target_cls, plot=plot, save_dir=save_dir, names=names, prefix="Mask" + )[2:] + + return { + "boxes": { + "p": results_boxes[0], + "r": results_boxes[1], + "ap": results_boxes[3], + "f1": results_boxes[2], + "ap_class": results_boxes[4], + }, + "masks": { + "p": results_masks[0], + "r": results_masks[1], + "ap": results_masks[3], + "f1": results_masks[2], + "ap_class": results_masks[4], + }, + } + + +class Metric: + def __init__(self) -> None: + """Initializes performance metric attributes for precision, recall, F1 score, average precision, and class + indices. + """ + self.p = [] # (nc, ) + self.r = [] # (nc, ) + self.f1 = [] # (nc, ) + self.all_ap = [] # (nc, 10) + self.ap_class_index = [] # (nc, ) + + @property + def ap50(self): + """ + AP@0.5 of all classes. + + Return: + (nc, ) or []. + """ + return self.all_ap[:, 0] if len(self.all_ap) else [] + + @property + def ap(self): + """AP@0.5:0.95 + Return: + (nc, ) or []. + """ + return self.all_ap.mean(1) if len(self.all_ap) else [] + + @property + def mp(self): + """ + Mean precision of all classes. + + Return: + float. + """ + return self.p.mean() if len(self.p) else 0.0 + + @property + def mr(self): + """ + Mean recall of all classes. + + Return: + float. + """ + return self.r.mean() if len(self.r) else 0.0 + + @property + def map50(self): + """ + Mean AP@0.5 of all classes. + + Return: + float. + """ + return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0 + + @property + def map(self): + """ + Mean AP@0.5:0.95 of all classes. + + Return: + float. + """ + return self.all_ap.mean() if len(self.all_ap) else 0.0 + + def mean_results(self): + """Mean of results, return mp, mr, map50, map.""" + return (self.mp, self.mr, self.map50, self.map) + + def class_result(self, i): + """Class-aware result, return p[i], r[i], ap50[i], ap[i]""" + return (self.p[i], self.r[i], self.ap50[i], self.ap[i]) + + def get_maps(self, nc): + """Calculates and returns mean Average Precision (mAP) for each class given number of classes `nc`.""" + maps = np.zeros(nc) + self.map + for i, c in enumerate(self.ap_class_index): + maps[c] = self.ap[i] + return maps + + def update(self, results): + """ + Args: + results: tuple(p, r, ap, f1, ap_class) + """ + p, r, all_ap, f1, ap_class_index = results + self.p = p + self.r = r + self.all_ap = all_ap + self.f1 = f1 + self.ap_class_index = ap_class_index + + +class Metrics: + """Metric for boxes and masks.""" + + def __init__(self) -> None: + """Initializes Metric objects for bounding boxes and masks to compute performance metrics in the Metrics + class. + """ + self.metric_box = Metric() + self.metric_mask = Metric() + + def update(self, results): + """ + Args: + results: Dict{'boxes': Dict{}, 'masks': Dict{}} + """ + self.metric_box.update(list(results["boxes"].values())) + self.metric_mask.update(list(results["masks"].values())) + + def mean_results(self): + """Computes and returns the mean results for both box and mask metrics by summing their individual means.""" + return self.metric_box.mean_results() + self.metric_mask.mean_results() + + def class_result(self, i): + """Returns the sum of box and mask metric results for a specified class index `i`.""" + return self.metric_box.class_result(i) + self.metric_mask.class_result(i) + + def get_maps(self, nc): + """Calculates and returns the sum of mean average precisions (mAPs) for both box and mask metrics for `nc` + classes. + """ + return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc) + + @property + def ap_class_index(self): + """Returns the class index for average precision, shared by both box and mask metrics.""" + return self.metric_box.ap_class_index + + +KEYS = [ + "train/box_loss", + "train/seg_loss", # train loss + "train/obj_loss", + "train/cls_loss", + "metrics/precision(B)", + "metrics/recall(B)", + "metrics/mAP_0.5(B)", + "metrics/mAP_0.5:0.95(B)", # metrics + "metrics/precision(M)", + "metrics/recall(M)", + "metrics/mAP_0.5(M)", + "metrics/mAP_0.5:0.95(M)", # metrics + "val/box_loss", + "val/seg_loss", # val loss + "val/obj_loss", + "val/cls_loss", + "x/lr0", + "x/lr1", + "x/lr2", +] + +BEST_KEYS = [ + "best/epoch", + "best/precision(B)", + "best/recall(B)", + "best/mAP_0.5(B)", + "best/mAP_0.5:0.95(B)", + "best/precision(M)", + "best/recall(M)", + "best/mAP_0.5(M)", + "best/mAP_0.5:0.95(M)", +] diff --git a/yolov5/utils/segment/plots.py b/yolov5/utils/segment/plots.py new file mode 100644 index 0000000000000000000000000000000000000000..f5b81711cc930ac8fe02833e294d4788249197b0 --- /dev/null +++ b/yolov5/utils/segment/plots.py @@ -0,0 +1,152 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license + +import contextlib +import math +from pathlib import Path + +import cv2 +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import torch + +from .. import threaded +from ..general import xywh2xyxy +from ..plots import Annotator, colors + + +@threaded +def plot_images_and_masks(images, targets, masks, paths=None, fname="images.jpg", names=None): + """Plots a grid of images, their labels, and masks with optional resizing and annotations, saving to fname.""" + if isinstance(images, torch.Tensor): + images = images.cpu().float().numpy() + if isinstance(targets, torch.Tensor): + targets = targets.cpu().numpy() + if isinstance(masks, torch.Tensor): + masks = masks.cpu().numpy().astype(int) + + max_size = 1920 # max image size + max_subplots = 16 # max image subplots, i.e. 4x4 + bs, _, h, w = images.shape # batch size, _, height, width + bs = min(bs, max_subplots) # limit plot images + ns = np.ceil(bs**0.5) # number of subplots (square) + if np.max(images[0]) <= 1: + images *= 255 # de-normalise (optional) + + # Build Image + mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init + for i, im in enumerate(images): + if i == max_subplots: # if last batch has fewer images than we expect + break + x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin + im = im.transpose(1, 2, 0) + mosaic[y : y + h, x : x + w, :] = im + + # Resize (optional) + scale = max_size / ns / max(h, w) + if scale < 1: + h = math.ceil(scale * h) + w = math.ceil(scale * w) + mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h))) + + # Annotate + fs = int((h + w) * ns * 0.01) # font size + annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names) + for i in range(i + 1): + x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin + annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders + if paths: + annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames + if len(targets) > 0: + idx = targets[:, 0] == i + ti = targets[idx] # image targets + + boxes = xywh2xyxy(ti[:, 2:6]).T + classes = ti[:, 1].astype("int") + labels = ti.shape[1] == 6 # labels if no conf column + conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred) + + if boxes.shape[1]: + if boxes.max() <= 1.01: # if normalized with tolerance 0.01 + boxes[[0, 2]] *= w # scale to pixels + boxes[[1, 3]] *= h + elif scale < 1: # absolute coords need scale if image scales + boxes *= scale + boxes[[0, 2]] += x + boxes[[1, 3]] += y + for j, box in enumerate(boxes.T.tolist()): + cls = classes[j] + color = colors(cls) + cls = names[cls] if names else cls + if labels or conf[j] > 0.25: # 0.25 conf thresh + label = f"{cls}" if labels else f"{cls} {conf[j]:.1f}" + annotator.box_label(box, label, color=color) + + # Plot masks + if len(masks): + if masks.max() > 1.0: # mean that masks are overlap + image_masks = masks[[i]] # (1, 640, 640) + nl = len(ti) + index = np.arange(nl).reshape(nl, 1, 1) + 1 + image_masks = np.repeat(image_masks, nl, axis=0) + image_masks = np.where(image_masks == index, 1.0, 0.0) + else: + image_masks = masks[idx] + + im = np.asarray(annotator.im).copy() + for j, box in enumerate(boxes.T.tolist()): + if labels or conf[j] > 0.25: # 0.25 conf thresh + color = colors(classes[j]) + mh, mw = image_masks[j].shape + if mh != h or mw != w: + mask = image_masks[j].astype(np.uint8) + mask = cv2.resize(mask, (w, h)) + mask = mask.astype(bool) + else: + mask = image_masks[j].astype(bool) + with contextlib.suppress(Exception): + im[y : y + h, x : x + w, :][mask] = ( + im[y : y + h, x : x + w, :][mask] * 0.4 + np.array(color) * 0.6 + ) + annotator.fromarray(im) + annotator.im.save(fname) # save + + +def plot_results_with_masks(file="path/to/results.csv", dir="", best=True): + """ + Plots training results from CSV files, plotting best or last result highlights based on `best` parameter. + + Example: from utils.plots import *; plot_results('path/to/results.csv') + """ + save_dir = Path(file).parent if file else Path(dir) + fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True) + ax = ax.ravel() + files = list(save_dir.glob("results*.csv")) + assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot." + for f in files: + try: + data = pd.read_csv(f) + index = np.argmax( + 0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] + 0.1 * data.values[:, 11] + ) + s = [x.strip() for x in data.columns] + x = data.values[:, 0] + for i, j in enumerate([1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]): + y = data.values[:, j] + # y[y == 0] = np.nan # don't show zero values + ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=2) + if best: + # best + ax[i].scatter(index, y[index], color="r", label=f"best:{index}", marker="*", linewidth=3) + ax[i].set_title(s[j] + f"\n{round(y[index], 5)}") + else: + # last + ax[i].scatter(x[-1], y[-1], color="r", label="last", marker="*", linewidth=3) + ax[i].set_title(s[j] + f"\n{round(y[-1], 5)}") + # if j in [8, 9, 10]: # share train and val loss y axes + # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) + except Exception as e: + print(f"Warning: Plotting error for {f}: {e}") + ax[1].legend() + fig.savefig(save_dir / "results.png", dpi=200) + plt.close() diff --git a/yolov5/utils/torch_utils.py b/yolov5/utils/torch_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..d15f1f73f6c35445ad2f5bb9d3940459a4c90e0f --- /dev/null +++ b/yolov5/utils/torch_utils.py @@ -0,0 +1,481 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""PyTorch utils.""" + +import math +import os +import platform +import subprocess +import time +import warnings +from contextlib import contextmanager +from copy import deepcopy +from pathlib import Path + +import torch +import torch.distributed as dist +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.parallel import DistributedDataParallel as DDP + +from utils.general import LOGGER, check_version, colorstr, file_date, git_describe + +LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv("RANK", -1)) +WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) + +try: + import thop # for FLOPs computation +except ImportError: + thop = None + +# Suppress PyTorch warnings +warnings.filterwarnings("ignore", message="User provided device_type of 'cuda', but CUDA is not available. Disabling") +warnings.filterwarnings("ignore", category=UserWarning) + + +def smart_inference_mode(torch_1_9=check_version(torch.__version__, "1.9.0")): + """Applies torch.inference_mode() if torch>=1.9.0, else torch.no_grad() as a decorator for functions.""" + + def decorate(fn): + """Applies torch.inference_mode() if torch>=1.9.0, else torch.no_grad() to the decorated function.""" + return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn) + + return decorate + + +def smartCrossEntropyLoss(label_smoothing=0.0): + """Returns a CrossEntropyLoss with optional label smoothing for torch>=1.10.0; warns if smoothing on lower + versions. + """ + if check_version(torch.__version__, "1.10.0"): + return nn.CrossEntropyLoss(label_smoothing=label_smoothing) + if label_smoothing > 0: + LOGGER.warning(f"WARNING ⚠️ label smoothing {label_smoothing} requires torch>=1.10.0") + return nn.CrossEntropyLoss() + + +def smart_DDP(model): + """Initializes DistributedDataParallel (DDP) for model training, respecting torch version constraints.""" + assert not check_version(torch.__version__, "1.12.0", pinned=True), ( + "torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. " + "Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395" + ) + if check_version(torch.__version__, "1.11.0"): + return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True) + else: + return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK) + + +def reshape_classifier_output(model, n=1000): + """Reshapes last layer of model to match class count 'n', supporting Classify, Linear, Sequential types.""" + from models.common import Classify + + name, m = list((model.model if hasattr(model, "model") else model).named_children())[-1] # last module + if isinstance(m, Classify): # YOLOv5 Classify() head + if m.linear.out_features != n: + m.linear = nn.Linear(m.linear.in_features, n) + elif isinstance(m, nn.Linear): # ResNet, EfficientNet + if m.out_features != n: + setattr(model, name, nn.Linear(m.in_features, n)) + elif isinstance(m, nn.Sequential): + types = [type(x) for x in m] + if nn.Linear in types: + i = len(types) - 1 - types[::-1].index(nn.Linear) # last nn.Linear index + if m[i].out_features != n: + m[i] = nn.Linear(m[i].in_features, n) + elif nn.Conv2d in types: + i = len(types) - 1 - types[::-1].index(nn.Conv2d) # last nn.Conv2d index + if m[i].out_channels != n: + m[i] = nn.Conv2d(m[i].in_channels, n, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None) + + +@contextmanager +def torch_distributed_zero_first(local_rank: int): + """Context manager ensuring ordered operations in distributed training by making all processes wait for the leading + process. + """ + if local_rank not in [-1, 0]: + dist.barrier(device_ids=[local_rank]) + yield + if local_rank == 0: + dist.barrier(device_ids=[0]) + + +def device_count(): + """Returns the number of available CUDA devices; works on Linux and Windows by invoking `nvidia-smi`.""" + assert platform.system() in ("Linux", "Windows"), "device_count() only supported on Linux or Windows" + try: + cmd = "nvidia-smi -L | wc -l" if platform.system() == "Linux" else 'nvidia-smi -L | find /c /v ""' # Windows + return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]) + except Exception: + return 0 + + +def select_device(device="", batch_size=0, newline=True): + """Selects computing device (CPU, CUDA GPU, MPS) for YOLOv5 model deployment, logging device info.""" + s = f"YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} " + device = str(device).strip().lower().replace("cuda:", "").replace("none", "") # to string, 'cuda:0' to '0' + cpu = device == "cpu" + mps = device == "mps" # Apple Metal Performance Shaders (MPS) + if cpu or mps: + os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # force torch.cuda.is_available() = False + elif device: # non-cpu device requested + os.environ["CUDA_VISIBLE_DEVICES"] = device # set environment variable - must be before assert is_available() + assert torch.cuda.is_available() and torch.cuda.device_count() >= len( + device.replace(",", "") + ), f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)" + + if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available + devices = device.split(",") if device else "0" # range(torch.cuda.device_count()) # i.e. 0,1,6,7 + n = len(devices) # device count + if n > 1 and batch_size > 0: # check batch_size is divisible by device_count + assert batch_size % n == 0, f"batch-size {batch_size} not multiple of GPU count {n}" + space = " " * (len(s) + 1) + for i, d in enumerate(devices): + p = torch.cuda.get_device_properties(i) + s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB + arg = "cuda:0" + elif mps and getattr(torch, "has_mps", False) and torch.backends.mps.is_available(): # prefer MPS if available + s += "MPS\n" + arg = "mps" + else: # revert to CPU + s += "CPU\n" + arg = "cpu" + + if not newline: + s = s.rstrip() + LOGGER.info(s) + return torch.device(arg) + + +def time_sync(): + """Synchronizes PyTorch for accurate timing, leveraging CUDA if available, and returns the current time.""" + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() + + +def profile(input, ops, n=10, device=None): + """YOLOv5 speed/memory/FLOPs profiler + Usage: + input = torch.randn(16, 3, 640, 640) + m1 = lambda x: x * torch.sigmoid(x) + m2 = nn.SiLU() + profile(input, [m1, m2], n=100) # profile over 100 iterations + """ + results = [] + if not isinstance(device, torch.device): + device = select_device(device) + print( + f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}" + f"{'input':>24s}{'output':>24s}" + ) + + for x in input if isinstance(input, list) else [input]: + x = x.to(device) + x.requires_grad = True + for m in ops if isinstance(ops, list) else [ops]: + m = m.to(device) if hasattr(m, "to") else m # device + m = m.half() if hasattr(m, "half") and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m + tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward + try: + flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1e9 * 2 # GFLOPs + except Exception: + flops = 0 + + try: + for _ in range(n): + t[0] = time_sync() + y = m(x) + t[1] = time_sync() + try: + _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward() + t[2] = time_sync() + except Exception: # no backward method + # print(e) # for debug + t[2] = float("nan") + tf += (t[1] - t[0]) * 1000 / n # ms per op forward + tb += (t[2] - t[1]) * 1000 / n # ms per op backward + mem = torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0 # (GB) + s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else "list" for x in (x, y)) # shapes + p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 # parameters + print(f"{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}") + results.append([p, flops, mem, tf, tb, s_in, s_out]) + except Exception as e: + print(e) + results.append(None) + torch.cuda.empty_cache() + return results + + +def is_parallel(model): + """Checks if the model is using Data Parallelism (DP) or Distributed Data Parallelism (DDP).""" + return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) + + +def de_parallel(model): + """Returns a single-GPU model by removing Data Parallelism (DP) or Distributed Data Parallelism (DDP) if applied.""" + return model.module if is_parallel(model) else model + + +def initialize_weights(model): + """Initializes weights of Conv2d, BatchNorm2d, and activations (Hardswish, LeakyReLU, ReLU, ReLU6, SiLU) in the + model. + """ + for m in model.modules(): + t = type(m) + if t is nn.Conv2d: + pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif t is nn.BatchNorm2d: + m.eps = 1e-3 + m.momentum = 0.03 + elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]: + m.inplace = True + + +def find_modules(model, mclass=nn.Conv2d): + """Finds and returns list of layer indices in `model.module_list` matching the specified `mclass`.""" + return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] + + +def sparsity(model): + """Calculates and returns the global sparsity of a model as the ratio of zero-valued parameters to total + parameters. + """ + a, b = 0, 0 + for p in model.parameters(): + a += p.numel() + b += (p == 0).sum() + return b / a + + +def prune(model, amount=0.3): + """Prunes Conv2d layers in a model to a specified sparsity using L1 unstructured pruning.""" + import torch.nn.utils.prune as prune + + for name, m in model.named_modules(): + if isinstance(m, nn.Conv2d): + prune.l1_unstructured(m, name="weight", amount=amount) # prune + prune.remove(m, "weight") # make permanent + LOGGER.info(f"Model pruned to {sparsity(model):.3g} global sparsity") + + +def fuse_conv_and_bn(conv, bn): + """ + Fuses Conv2d and BatchNorm2d layers into a single Conv2d layer. + + See https://tehnokv.com/posts/fusing-batchnorm-and-conv/. + """ + fusedconv = ( + nn.Conv2d( + conv.in_channels, + conv.out_channels, + kernel_size=conv.kernel_size, + stride=conv.stride, + padding=conv.padding, + dilation=conv.dilation, + groups=conv.groups, + bias=True, + ) + .requires_grad_(False) + .to(conv.weight.device) + ) + + # Prepare filters + w_conv = conv.weight.clone().view(conv.out_channels, -1) + w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) + fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape)) + + # Prepare spatial bias + b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias + b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps)) + fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn) + + return fusedconv + + +def model_info(model, verbose=False, imgsz=640): + """ + Prints model summary including layers, parameters, gradients, and FLOPs; imgsz may be int or list. + + Example: img_size=640 or img_size=[640, 320] + """ + n_p = sum(x.numel() for x in model.parameters()) # number parameters + n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients + if verbose: + print(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}") + for i, (name, p) in enumerate(model.named_parameters()): + name = name.replace("module_list.", "") + print( + "%5g %40s %9s %12g %20s %10.3g %10.3g" + % (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()) + ) + + try: # FLOPs + p = next(model.parameters()) + stride = max(int(model.stride.max()), 32) if hasattr(model, "stride") else 32 # max stride + im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format + flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1e9 * 2 # stride GFLOPs + imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float + fs = f", {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs" # 640x640 GFLOPs + except Exception: + fs = "" + + name = Path(model.yaml_file).stem.replace("yolov5", "YOLOv5") if hasattr(model, "yaml_file") else "Model" + LOGGER.info(f"{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}") + + +def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) + """Scales an image tensor `img` of shape (bs,3,y,x) by `ratio`, optionally maintaining the original shape, padded to + multiples of `gs`. + """ + if ratio == 1.0: + return img + h, w = img.shape[2:] + s = (int(h * ratio), int(w * ratio)) # new size + img = F.interpolate(img, size=s, mode="bilinear", align_corners=False) # resize + if not same_shape: # pad/crop img + h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w)) + return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean + + +def copy_attr(a, b, include=(), exclude=()): + """Copies attributes from object b to a, optionally filtering with include and exclude lists.""" + for k, v in b.__dict__.items(): + if (len(include) and k not in include) or k.startswith("_") or k in exclude: + continue + else: + setattr(a, k, v) + + +def smart_optimizer(model, name="Adam", lr=0.001, momentum=0.9, decay=1e-5): + """ + Initializes YOLOv5 smart optimizer with 3 parameter groups for different decay configurations. + + Groups are 0) weights with decay, 1) weights no decay, 2) biases no decay. + """ + g = [], [], [] # optimizer parameter groups + bn = tuple(v for k, v in nn.__dict__.items() if "Norm" in k) # normalization layers, i.e. BatchNorm2d() + for v in model.modules(): + for p_name, p in v.named_parameters(recurse=0): + if p_name == "bias": # bias (no decay) + g[2].append(p) + elif p_name == "weight" and isinstance(v, bn): # weight (no decay) + g[1].append(p) + else: + g[0].append(p) # weight (with decay) + + if name == "Adam": + optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999)) # adjust beta1 to momentum + elif name == "AdamW": + optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0) + elif name == "RMSProp": + optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum) + elif name == "SGD": + optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True) + else: + raise NotImplementedError(f"Optimizer {name} not implemented.") + + optimizer.add_param_group({"params": g[0], "weight_decay": decay}) # add g0 with weight_decay + optimizer.add_param_group({"params": g[1], "weight_decay": 0.0}) # add g1 (BatchNorm2d weights) + LOGGER.info( + f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups " + f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias' + ) + return optimizer + + +def smart_hub_load(repo="ultralytics/yolov5", model="yolov5s", **kwargs): + """YOLOv5 torch.hub.load() wrapper with smart error handling, adjusting torch arguments for compatibility.""" + if check_version(torch.__version__, "1.9.1"): + kwargs["skip_validation"] = True # validation causes GitHub API rate limit errors + if check_version(torch.__version__, "1.12.0"): + kwargs["trust_repo"] = True # argument required starting in torch 0.12 + try: + return torch.hub.load(repo, model, **kwargs) + except Exception: + return torch.hub.load(repo, model, force_reload=True, **kwargs) + + +def smart_resume(ckpt, optimizer, ema=None, weights="yolov5s.pt", epochs=300, resume=True): + """Resumes training from a checkpoint, updating optimizer, ema, and epochs, with optional resume verification.""" + best_fitness = 0.0 + start_epoch = ckpt["epoch"] + 1 + if ckpt["optimizer"] is not None: + optimizer.load_state_dict(ckpt["optimizer"]) # optimizer + best_fitness = ckpt["best_fitness"] + if ema and ckpt.get("ema"): + ema.ema.load_state_dict(ckpt["ema"].float().state_dict()) # EMA + ema.updates = ckpt["updates"] + if resume: + assert start_epoch > 0, ( + f"{weights} training to {epochs} epochs is finished, nothing to resume.\n" + f"Start a new training without --resume, i.e. 'python train.py --weights {weights}'" + ) + LOGGER.info(f"Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs") + if epochs < start_epoch: + LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.") + epochs += ckpt["epoch"] # finetune additional epochs + return best_fitness, start_epoch, epochs + + +class EarlyStopping: + # YOLOv5 simple early stopper + def __init__(self, patience=30): + """Initializes simple early stopping mechanism for YOLOv5, with adjustable patience for non-improving epochs.""" + self.best_fitness = 0.0 # i.e. mAP + self.best_epoch = 0 + self.patience = patience or float("inf") # epochs to wait after fitness stops improving to stop + self.possible_stop = False # possible stop may occur next epoch + + def __call__(self, epoch, fitness): + """Evaluates if training should stop based on fitness improvement and patience, returning a boolean.""" + if fitness >= self.best_fitness: # >= 0 to allow for early zero-fitness stage of training + self.best_epoch = epoch + self.best_fitness = fitness + delta = epoch - self.best_epoch # epochs without improvement + self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch + stop = delta >= self.patience # stop training if patience exceeded + if stop: + LOGGER.info( + f"Stopping training early as no improvement observed in last {self.patience} epochs. " + f"Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n" + f"To update EarlyStopping(patience={self.patience}) pass a new patience value, " + f"i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping." + ) + return stop + + +class ModelEMA: + """Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models + Keeps a moving average of everything in the model state_dict (parameters and buffers) + For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage + """ + + def __init__(self, model, decay=0.9999, tau=2000, updates=0): + """Initializes EMA with model parameters, decay rate, tau for decay adjustment, and update count; sets model to + evaluation mode. + """ + self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA + self.updates = updates # number of EMA updates + self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs) + for p in self.ema.parameters(): + p.requires_grad_(False) + + def update(self, model): + """Updates the Exponential Moving Average (EMA) parameters based on the current model's parameters.""" + self.updates += 1 + d = self.decay(self.updates) + + msd = de_parallel(model).state_dict() # model state_dict + for k, v in self.ema.state_dict().items(): + if v.dtype.is_floating_point: # true for FP16 and FP32 + v *= d + v += (1 - d) * msd[k].detach() + # assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype} and model {msd[k].dtype} must be FP32' + + def update_attr(self, model, include=(), exclude=("process_group", "reducer")): + """Updates EMA attributes by copying specified attributes from model to EMA, excluding certain attributes by + default. + """ + copy_attr(self.ema, model, include, exclude) diff --git a/yolov5/utils/triton.py b/yolov5/utils/triton.py new file mode 100644 index 0000000000000000000000000000000000000000..3d529ec88a077eebb41f0bd9ea17733fca19e087 --- /dev/null +++ b/yolov5/utils/triton.py @@ -0,0 +1,91 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +"""Utils to interact with the Triton Inference Server.""" + +import typing +from urllib.parse import urlparse + +import torch + + +class TritonRemoteModel: + """ + A wrapper over a model served by the Triton Inference Server. + + It can be configured to communicate over GRPC or HTTP. It accepts Torch Tensors as input and returns them as + outputs. + """ + + def __init__(self, url: str): + """ + Keyword arguments: + url: Fully qualified address of the Triton server - for e.g. grpc://localhost:8000 + """ + + parsed_url = urlparse(url) + if parsed_url.scheme == "grpc": + from tritonclient.grpc import InferenceServerClient, InferInput + + self.client = InferenceServerClient(parsed_url.netloc) # Triton GRPC client + model_repository = self.client.get_model_repository_index() + self.model_name = model_repository.models[0].name + self.metadata = self.client.get_model_metadata(self.model_name, as_json=True) + + def create_input_placeholders() -> typing.List[InferInput]: + return [ + InferInput(i["name"], [int(s) for s in i["shape"]], i["datatype"]) for i in self.metadata["inputs"] + ] + + else: + from tritonclient.http import InferenceServerClient, InferInput + + self.client = InferenceServerClient(parsed_url.netloc) # Triton HTTP client + model_repository = self.client.get_model_repository_index() + self.model_name = model_repository[0]["name"] + self.metadata = self.client.get_model_metadata(self.model_name) + + def create_input_placeholders() -> typing.List[InferInput]: + return [ + InferInput(i["name"], [int(s) for s in i["shape"]], i["datatype"]) for i in self.metadata["inputs"] + ] + + self._create_input_placeholders_fn = create_input_placeholders + + @property + def runtime(self): + """Returns the model runtime.""" + return self.metadata.get("backend", self.metadata.get("platform")) + + def __call__(self, *args, **kwargs) -> typing.Union[torch.Tensor, typing.Tuple[torch.Tensor, ...]]: + """ + Invokes the model. + + Parameters can be provided via args or kwargs. args, if provided, are assumed to match the order of inputs of + the model. kwargs are matched with the model input names. + """ + inputs = self._create_inputs(*args, **kwargs) + response = self.client.infer(model_name=self.model_name, inputs=inputs) + result = [] + for output in self.metadata["outputs"]: + tensor = torch.as_tensor(response.as_numpy(output["name"])) + result.append(tensor) + return result[0] if len(result) == 1 else result + + def _create_inputs(self, *args, **kwargs): + """Creates input tensors from args or kwargs, not both; raises error if none or both are provided.""" + args_len, kwargs_len = len(args), len(kwargs) + if not args_len and not kwargs_len: + raise RuntimeError("No inputs provided.") + if args_len and kwargs_len: + raise RuntimeError("Cannot specify args and kwargs at the same time") + + placeholders = self._create_input_placeholders_fn() + if args_len: + if args_len != len(placeholders): + raise RuntimeError(f"Expected {len(placeholders)} inputs, got {args_len}.") + for input, value in zip(placeholders, args): + input.set_data_from_numpy(value.cpu().numpy()) + else: + for input in placeholders: + value = kwargs[input.name] + input.set_data_from_numpy(value.cpu().numpy()) + return placeholders diff --git a/yolov5/val.py b/yolov5/val.py new file mode 100644 index 0000000000000000000000000000000000000000..c1e8a6aa309432609f5f68c73b0e14f53bcdb55a --- /dev/null +++ b/yolov5/val.py @@ -0,0 +1,439 @@ +# Ultralytics YOLOv5 🚀, AGPL-3.0 license +""" +Validate a trained YOLOv5 detection model on a detection dataset. + +Usage: + $ python val.py --weights yolov5s.pt --data coco128.yaml --img 640 + +Usage - formats: + $ python val.py --weights yolov5s.pt # PyTorch + yolov5s.torchscript # TorchScript + yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn + yolov5s_openvino_model # OpenVINO + yolov5s.engine # TensorRT + yolov5s.mlmodel # CoreML (macOS-only) + yolov5s_saved_model # TensorFlow SavedModel + yolov5s.pb # TensorFlow GraphDef + yolov5s.tflite # TensorFlow Lite + yolov5s_edgetpu.tflite # TensorFlow Edge TPU + yolov5s_paddle_model # PaddlePaddle +""" + +import argparse +import json +import os +import subprocess +import sys +from pathlib import Path + +import numpy as np +import torch +from tqdm import tqdm + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[0] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import DetectMultiBackend +from utils.callbacks import Callbacks +from utils.dataloaders import create_dataloader +from utils.general import ( + LOGGER, + TQDM_BAR_FORMAT, + Profile, + check_dataset, + check_img_size, + check_requirements, + check_yaml, + coco80_to_coco91_class, + colorstr, + increment_path, + non_max_suppression, + print_args, + scale_boxes, + xywh2xyxy, + xyxy2xywh, +) +from utils.metrics import ConfusionMatrix, ap_per_class, box_iou +from utils.plots import output_to_target, plot_images, plot_val_study +from utils.torch_utils import select_device, smart_inference_mode + + +def save_one_txt(predn, save_conf, shape, file): + """Saves one detection result to a txt file in normalized xywh format, optionally including confidence.""" + gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh + for *xyxy, conf, cls in predn.tolist(): + xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format + with open(file, "a") as f: + f.write(("%g " * len(line)).rstrip() % line + "\n") + + +def save_one_json(predn, jdict, path, class_map): + """ + Saves one JSON detection result with image ID, category ID, bounding box, and score. + + Example: {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236} + """ + image_id = int(path.stem) if path.stem.isnumeric() else path.stem + box = xyxy2xywh(predn[:, :4]) # xywh + box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner + for p, b in zip(predn.tolist(), box.tolist()): + jdict.append( + { + "image_id": image_id, + "category_id": class_map[int(p[5])], + "bbox": [round(x, 3) for x in b], + "score": round(p[4], 5), + } + ) + + +def process_batch(detections, labels, iouv): + """ + Return correct prediction matrix. + + Arguments: + detections (array[N, 6]), x1, y1, x2, y2, conf, class + labels (array[M, 5]), class, x1, y1, x2, y2 + Returns: + correct (array[N, 10]), for 10 IoU levels + """ + correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool) + iou = box_iou(labels[:, 1:], detections[:, :4]) + correct_class = labels[:, 0:1] == detections[:, 5] + for i in range(len(iouv)): + x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match + if x[0].shape[0]: + matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou] + if x[0].shape[0] > 1: + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 1], return_index=True)[1]] + # matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 0], return_index=True)[1]] + correct[matches[:, 1].astype(int), i] = True + return torch.tensor(correct, dtype=torch.bool, device=iouv.device) + + +@smart_inference_mode() +def run( + data, + weights=None, # model.pt path(s) + batch_size=32, # batch size + imgsz=640, # inference size (pixels) + conf_thres=0.001, # confidence threshold + iou_thres=0.6, # NMS IoU threshold + max_det=300, # maximum detections per image + task="val", # train, val, test, speed or study + device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu + workers=8, # max dataloader workers (per RANK in DDP mode) + single_cls=False, # treat as single-class dataset + augment=False, # augmented inference + verbose=False, # verbose output + save_txt=False, # save results to *.txt + save_hybrid=False, # save label+prediction hybrid results to *.txt + save_conf=False, # save confidences in --save-txt labels + save_json=False, # save a COCO-JSON results file + project=ROOT / "runs/val", # save to project/name + name="exp", # save to project/name + exist_ok=False, # existing project/name ok, do not increment + half=True, # use FP16 half-precision inference + dnn=False, # use OpenCV DNN for ONNX inference + model=None, + dataloader=None, + save_dir=Path(""), + plots=True, + callbacks=Callbacks(), + compute_loss=None, +): + """Evaluates model on a dataset and logs performance metrics, results are saved to specific directories.""" + # Initialize/load model and set device + training = model is not None + if training: # called by train.py + device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model + half &= device.type != "cpu" # half precision only supported on CUDA + model.half() if half else model.float() + else: # called directly + device = select_device(device, batch_size=batch_size) + + # Directories + save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run + (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) + stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine + imgsz = check_img_size(imgsz, s=stride) # check image size + half = model.fp16 # FP16 supported on limited backends with CUDA + if engine: + batch_size = model.batch_size + else: + device = model.device + if not (pt or jit): + batch_size = 1 # export.py models default to batch-size 1 + LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models") + + # Data + data = check_dataset(data) # check + + # Configure + model.eval() + cuda = device.type != "cpu" + is_coco = isinstance(data.get("val"), str) and data["val"].endswith(f"coco{os.sep}val2017.txt") # COCO dataset + nc = 1 if single_cls else int(data["nc"]) # number of classes + iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95 + niou = iouv.numel() + + # Dataloader + if not training: + if pt and not single_cls: # check --weights are trained on --data + ncm = model.model.nc + assert ncm == nc, ( + f"{weights} ({ncm} classes) trained on different --data than what you passed ({nc} " + f"classes). Pass correct combination of --weights and --data that are trained together." + ) + model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup + pad, rect = (0.0, False) if task == "speed" else (0.5, pt) # square inference for benchmarks + task = task if task in ("train", "val", "test") else "val" # path to train/val/test images + dataloader = create_dataloader( + data[task], + imgsz, + batch_size, + stride, + single_cls, + pad=pad, + rect=rect, + workers=workers, + prefix=colorstr(f"{task}: "), + )[0] + + seen = 0 + confusion_matrix = ConfusionMatrix(nc=nc) + names = model.names if hasattr(model, "names") else model.module.names # get class names + if isinstance(names, (list, tuple)): # old format + names = dict(enumerate(names)) + class_map = coco80_to_coco91_class() if is_coco else list(range(1000)) + s = ("%22s" + "%11s" * 6) % ("Class", "Images", "Instances", "P", "R", "mAP50", "mAP50-95") + tp, fp, p, r, f1, mp, mr, map50, ap50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 + dt = Profile(device=device), Profile(device=device), Profile(device=device) # profiling times + loss = torch.zeros(3, device=device) + jdict, stats, ap, ap_class = [], [], [], [] + callbacks.run("on_val_start") + pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar + for batch_i, (im, targets, paths, shapes) in enumerate(pbar): + callbacks.run("on_val_batch_start") + with dt[0]: + if cuda: + im = im.to(device, non_blocking=True) + targets = targets.to(device) + im = im.half() if half else im.float() # uint8 to fp16/32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + nb, _, height, width = im.shape # batch size, channels, height, width + + # Inference + with dt[1]: + preds, train_out = model(im) if compute_loss else (model(im, augment=augment), None) + + # Loss + if compute_loss: + loss += compute_loss(train_out, targets)[1] # box, obj, cls + + # NMS + targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels + lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling + with dt[2]: + preds = non_max_suppression( + preds, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls, max_det=max_det + ) + + # Metrics + for si, pred in enumerate(preds): + labels = targets[targets[:, 0] == si, 1:] + nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions + path, shape = Path(paths[si]), shapes[si][0] + correct = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init + seen += 1 + + if npr == 0: + if nl: + stats.append((correct, *torch.zeros((2, 0), device=device), labels[:, 0])) + if plots: + confusion_matrix.process_batch(detections=None, labels=labels[:, 0]) + continue + + # Predictions + if single_cls: + pred[:, 5] = 0 + predn = pred.clone() + scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred + + # Evaluate + if nl: + tbox = xywh2xyxy(labels[:, 1:5]) # target boxes + scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels + labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels + correct = process_batch(predn, labelsn, iouv) + if plots: + confusion_matrix.process_batch(predn, labelsn) + stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0])) # (correct, conf, pcls, tcls) + + # Save/log + if save_txt: + (save_dir / "labels").mkdir(parents=True, exist_ok=True) + save_one_txt(predn, save_conf, shape, file=save_dir / "labels" / f"{path.stem}.txt") + if save_json: + save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary + callbacks.run("on_val_image_end", pred, predn, path, names, im[si]) + + # Plot images + if plots and batch_i < 3: + plot_images(im, targets, paths, save_dir / f"val_batch{batch_i}_labels.jpg", names) # labels + plot_images(im, output_to_target(preds), paths, save_dir / f"val_batch{batch_i}_pred.jpg", names) # pred + + callbacks.run("on_val_batch_end", batch_i, im, targets, paths, shapes, preds) + + # Compute metrics + stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy + if len(stats) and stats[0].any(): + tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names) + ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95 + mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean() + nt = np.bincount(stats[3].astype(int), minlength=nc) # number of targets per class + + # Print results + pf = "%22s" + "%11i" * 2 + "%11.3g" * 4 # print format + LOGGER.info(pf % ("all", seen, nt.sum(), mp, mr, map50, map)) + if nt.sum() == 0: + LOGGER.warning(f"WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels") + + # Print results per class + if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): + for i, c in enumerate(ap_class): + LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i])) + + # Print speeds + t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image + if not training: + shape = (batch_size, 3, imgsz, imgsz) + LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}" % t) + + # Plots + if plots: + confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) + callbacks.run("on_val_end", nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) + + # Save JSON + if save_json and len(jdict): + w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else "" # weights + anno_json = str(Path("../datasets/coco/annotations/instances_val2017.json")) # annotations + if not os.path.exists(anno_json): + anno_json = os.path.join(data["path"], "annotations", "instances_val2017.json") + pred_json = str(save_dir / f"{w}_predictions.json") # predictions + LOGGER.info(f"\nEvaluating pycocotools mAP... saving {pred_json}...") + with open(pred_json, "w") as f: + json.dump(jdict, f) + + try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb + check_requirements("pycocotools>=2.0.6") + from pycocotools.coco import COCO + from pycocotools.cocoeval import COCOeval + + anno = COCO(anno_json) # init annotations api + pred = anno.loadRes(pred_json) # init predictions api + eval = COCOeval(anno, pred, "bbox") + if is_coco: + eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # image IDs to evaluate + eval.evaluate() + eval.accumulate() + eval.summarize() + map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5) + except Exception as e: + LOGGER.info(f"pycocotools unable to run: {e}") + + # Return results + model.float() # for training + if not training: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else "" + LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") + maps = np.zeros(nc) + map + for i, c in enumerate(ap_class): + maps[c] = ap[i] + return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t + + +def parse_opt(): + """Parses command-line options for YOLOv5 model inference configuration.""" + parser = argparse.ArgumentParser() + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") + parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model path(s)") + parser.add_argument("--batch-size", type=int, default=32, help="batch size") + parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)") + parser.add_argument("--conf-thres", type=float, default=0.001, help="confidence threshold") + parser.add_argument("--iou-thres", type=float, default=0.6, help="NMS IoU threshold") + parser.add_argument("--max-det", type=int, default=300, help="maximum detections per image") + parser.add_argument("--task", default="val", help="train, val, test, speed or study") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") + parser.add_argument("--single-cls", action="store_true", help="treat as single-class dataset") + parser.add_argument("--augment", action="store_true", help="augmented inference") + parser.add_argument("--verbose", action="store_true", help="report mAP by class") + parser.add_argument("--save-txt", action="store_true", help="save results to *.txt") + parser.add_argument("--save-hybrid", action="store_true", help="save label+prediction hybrid results to *.txt") + parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels") + parser.add_argument("--save-json", action="store_true", help="save a COCO-JSON results file") + parser.add_argument("--project", default=ROOT / "runs/val", help="save to project/name") + parser.add_argument("--name", default="exp", help="save to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference") + parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference") + opt = parser.parse_args() + opt.data = check_yaml(opt.data) # check YAML + opt.save_json |= opt.data.endswith("coco.yaml") + opt.save_txt |= opt.save_hybrid + print_args(vars(opt)) + return opt + + +def main(opt): + """Executes YOLOv5 tasks like training, validation, testing, speed, and study benchmarks based on provided + options. + """ + check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop")) + + if opt.task in ("train", "val", "test"): # run normally + if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466 + LOGGER.info(f"WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results") + if opt.save_hybrid: + LOGGER.info("WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone") + run(**vars(opt)) + + else: + weights = opt.weights if isinstance(opt.weights, list) else [opt.weights] + opt.half = torch.cuda.is_available() and opt.device != "cpu" # FP16 for fastest results + if opt.task == "speed": # speed benchmarks + # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt... + opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False + for opt.weights in weights: + run(**vars(opt), plots=False) + + elif opt.task == "study": # speed vs mAP benchmarks + # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt... + for opt.weights in weights: + f = f"study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt" # filename to save to + x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis + for opt.imgsz in x: # img-size + LOGGER.info(f"\nRunning {f} --imgsz {opt.imgsz}...") + r, _, t = run(**vars(opt), plots=False) + y.append(r + t) # results and times + np.savetxt(f, y, fmt="%10.4g") # save + subprocess.run(["zip", "-r", "study.zip", "study_*.txt"]) + plot_val_study(x=x) # plot + else: + raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")') + + +if __name__ == "__main__": + opt = parse_opt() + main(opt) diff --git a/yolov5s.pt b/yolov5s.pt new file mode 100644 index 0000000000000000000000000000000000000000..cd69a5e14d396322319b06304274238c0188a522 --- /dev/null +++ b/yolov5s.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8b3b748c1e592ddd8868022e8732fde20025197328490623cc16c6f24d0782ee +size 14808437