commit c3731591aff0e65aeb375a2b0f756bb87a03ccd8 Author: Glenn Jocher Date: Sun Aug 26 10:51:39 2018 +0200 Initial commit diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 00000000..dfe07704 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +# Auto detect text files and perform LF normalization +* text=auto diff --git a/.gitignore b/.gitignore new file mode 100755 index 00000000..774b1577 --- /dev/null +++ b/.gitignore @@ -0,0 +1,214 @@ +# Repo-specific GitIgnore ---------------------------------------------------------------------------------------------- +*.jpg +*.png +*.bmp +*.tif +*.heic +*.JPG +*.PNG +*.TIF +*.HEIC +*.weights +*.pt +*.tif.txt +!zidane_result.jpg +!coco_training_loss.png +!images/* + +checkpoints +temp-plot.html + +# MATLAB GitIgnore ----------------------------------------------------------------------------------------------------- +*.m~ +*.mat +!targets*.mat + +# GitHub Python GitIgnore ---------------------------------------------------------------------------------------------- +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv +venv/ +ENV/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + + +# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore ----------------------------------------------- + +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon +Icon? + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + + +# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff: +.idea/* +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/dictionaries +.html # Bokeh Plots +.pg # TensorFlow Frozen Graphs +.avi # videos + +# Sensitive or high-churn files: +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml + +# Gradle: +.idea/**/gradle.xml +.idea/**/libraries + +# CMake +cmake-build-debug/ +cmake-build-release/ + +# Mongo Explorer plugin: +.idea/**/mongoSettings.xml + +## File-based project format: +*.iws + +## Plugin-specific files: + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..9e419e04 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ +GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100755 index 00000000..0c0f689f --- /dev/null +++ b/README.md @@ -0,0 +1,32 @@ + + +# Introduction + +This directory contains software developed by Ultralytics LLC. For more information on Ultralytics projects please visit: +http://www.ultralytics.com   + +# Description + +The https://github.com/ultralytics/yolov3 repo contains code to train YOLOv3 on the COCO dataset: https://cocodataset.org/#home. Credit to P.J. Reddie for YOLO (https://pjreddie.com/darknet/yolo/) and to Erik Lindernoren for the pytorch implementation this repo is based on (https://github.com/eriklindernoren/PyTorch-YOLOv3). + +# Requirements + +Python 3.6 or later with the following `pip3 install -U -r requirements.txt` packages: + +- `numpy` +- `torch` +- `opencv-python` + +# Running + +Run `train.py` to begin training. Each epoch trains on 120,000 images from the train and validate sets, and validates on 5000 images in the validation set. An Nvidia GTX 1080 Ti will run about 16 epochs per day. Loss plots for the bounding boxes, objectness and class confidence should appear similar to results shown here. +![Alt](https://github.com/ultralytics/yolov3/blob/master/data/xview_training_loss.png "training loss") + +Checkpoints will be saved in `/checkpoints` directory. Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder, shown here. +![Alt](https://github.com/ultralytics/yolov3/blob/master/data/zidane_result.jpg "example") + +Run `test.py` to test the latest checkpoint on the 5000 validation images. Joseph Redmon's official YOLOv3 weights produce a mAP of .581 using this method, compared to .579 in his paper. + +# Contact + +For questions or comments please contact Glenn Jocher at glenn.jocher@ultralytics.com or visit us at http://www.ultralytics.com/contact \ No newline at end of file diff --git a/cfg/coco.data b/cfg/coco.data new file mode 100644 index 00000000..785b5e25 --- /dev/null +++ b/cfg/coco.data @@ -0,0 +1,6 @@ +classes=80 +train=/Users/glennjocher/Downloads/DATA/coco/trainvalno5k.txt +valid=/Users/glennjocher/Downloads/DATA/coco/5k.txt +names=data/coco.names +backup=backup/ +eval=coco diff --git a/cfg/yolov3.cfg b/cfg/yolov3.cfg new file mode 100755 index 00000000..946e0154 --- /dev/null +++ b/cfg/yolov3.cfg @@ -0,0 +1,788 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=16 +subdivisions=1 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 diff --git a/data/coco.names b/data/coco.names new file mode 100755 index 00000000..ca76c80b --- /dev/null +++ b/data/coco.names @@ -0,0 +1,80 @@ +person +bicycle +car +motorbike +aeroplane +bus +train +truck +boat +traffic light +fire hydrant +stop sign +parking meter +bench +bird +cat +dog +horse +sheep +cow +elephant +bear +zebra +giraffe +backpack +umbrella +handbag +tie +suitcase +frisbee +skis +snowboard +sports ball +kite +baseball bat +baseball glove +skateboard +surfboard +tennis racket +bottle +wine glass +cup +fork +knife +spoon +bowl +banana +apple +sandwich +orange +broccoli +carrot +hot dog +pizza +donut +cake +chair +sofa +pottedplant +bed +diningtable +toilet +tvmonitor +laptop +mouse +remote +keyboard +cell phone +microwave +oven +toaster +sink +refrigerator +book +clock +vase +scissors +teddy bear +hair drier +toothbrush diff --git a/data/coco_training_loss.png b/data/coco_training_loss.png new file mode 100644 index 00000000..35597edb Binary files /dev/null and b/data/coco_training_loss.png differ diff --git a/data/get_coco_dataset.sh b/data/get_coco_dataset.sh new file mode 100755 index 00000000..8a892633 --- /dev/null +++ b/data/get_coco_dataset.sh @@ -0,0 +1,40 @@ +#!/bin/bash + +# CREDIT: https://github.com/pjreddie/darknet/tree/master/scripts/get_coco_dataset.sh + +# Clone COCO API +git clone https://github.com/pdollar/coco +cd coco + +mkdir images +cd images + +# Download Images +wget -c https://pjreddie.com/media/files/train2014.zip +wget -c https://pjreddie.com/media/files/val2014.zip + +# Unzip +unzip -q train2014.zip +unzip -q val2014.zip + +cd .. + +# Download COCO Metadata +wget -c https://pjreddie.com/media/files/instances_train-val2014.zip +wget -c https://pjreddie.com/media/files/coco/5k.part +wget -c https://pjreddie.com/media/files/coco/trainvalno5k.part +wget -c https://pjreddie.com/media/files/coco/labels.tgz +tar xzf labels.tgz +unzip -q instances_train-val2014.zip + +# Set Up Image Lists +#paste <(awk "{print \"$PWD\"}" <5k.part) 5k.part | tr -d '\t' > 5k.txt +#paste <(awk "{print \"$PWD\"}" trainvalno5k.txt + +sudo shutdown + +# get xview training data +# wget -O train_images.tgz 'https://d307kc0mrhucc3.cloudfront.net/train_images.tgz?Expires=1530124049&Signature=JrQoxipmsETvb7eQHCfDFUO-QEHJGAayUv0i-ParmS-1hn7hl9D~bzGuHWG82imEbZSLUARTtm0wOJ7EmYMGmG5PtLKz9H5qi6DjoSUuFc13NQ-~6yUhE~NfPaTnehUdUMCa3On2wl1h1ZtRG~0Jq1P-AJbpe~oQxbyBrs1KccaMa7FK4F4oMM6sMnNgoXx8-3O77kYw~uOpTMFmTaQdHln6EztW0Lx17i57kK3ogbSUpXgaUTqjHCRA1dWIl7PY1ngQnLslkLhZqmKcaL-BvWf0ZGjHxCDQBpnUjIlvMu5NasegkwD9Jjc0ClgTxsttSkmbapVqaVC8peR0pO619Q__&Key-Pair-Id=APKAIKGDJB5C3XUL2DXQ' +# tar -xvzf train_images.tgz +# sudo rm -rf train_images/._* +# lastly convert each .tif to a .bmp for faster loading in cv2 diff --git a/data/zidane_result.jpg b/data/zidane_result.jpg new file mode 100644 index 00000000..966bd350 Binary files /dev/null and b/data/zidane_result.jpg differ diff --git a/detect.py b/detect.py new file mode 100755 index 00000000..e93312d3 --- /dev/null +++ b/detect.py @@ -0,0 +1,151 @@ +import argparse +import time + +from models import * +from utils.datasets import * +from utils.utils import * + +cuda = torch.cuda.is_available() +device = torch.device('cuda:0' if cuda else 'cpu') + +parser = argparse.ArgumentParser() +# Get data configuration + +# cd yolo && python3 detect.py -secondary_classifier 1 +parser.add_argument('-image_folder', type=str, default='data/samples', help='path to images') +parser.add_argument('-output_folder', type=str, default='output', help='path to outputs') +parser.add_argument('-plot_flag', type=bool, default=True) +parser.add_argument('-txt_out', type=bool, default=False) + +parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path') +parser.add_argument('-class_path', type=str, default='data/coco.names', help='path to class label file') +parser.add_argument('-conf_thres', type=float, default=0.8, help='object confidence threshold') +parser.add_argument('-nms_thres', type=float, default=0.5, help='iou threshold for non-maximum suppression') +parser.add_argument('-batch_size', type=int, default=1, help='size of the batches') +parser.add_argument('-img_size', type=int, default=32 * 13, help='size of each image dimension') +opt = parser.parse_args() +print(opt) + + +def detect(opt): + os.system('rm -rf ' + opt.output_folder) + os.makedirs(opt.output_folder, exist_ok=True) + + # Load model + model = Darknet(opt.cfg, opt.img_size) + + weights_path = 'checkpoints/yolov3.weights' + if weights_path.endswith('.weights'): # saved in darknet format + load_weights(model, weights_path) + else: # endswith('.pt'), saved in pytorch format + checkpoint = torch.load(weights_path, map_location='cpu') + model.load_state_dict(checkpoint['model']) + del checkpoint + + # current = model.state_dict() + # saved = checkpoint['model'] + # # 1. filter out unnecessary keys + # saved = {k: v for k, v in saved.items() if ((k in current) and (current[k].shape == v.shape))} + # # 2. overwrite entries in the existing state dict + # current.update(saved) + # # 3. load the new state dict + # model.load_state_dict(current) + # model.to(device).eval() + # del checkpoint, current, saved + + model.to(device).eval() + + # Set Dataloader + classes = load_classes(opt.class_path) # Extracts class labels from file + dataloader = ImageFolder(opt.image_folder, batch_size=opt.batch_size, img_size=opt.img_size) + + imgs = [] # Stores image paths + img_detections = [] # Stores detections for each image index + prev_time = time.time() + detections = None + for batch_i, (img_paths, img) in enumerate(dataloader): + print(batch_i, img.shape, end=' ') + preds = [] + + # Get detections + with torch.no_grad(): + # Normal orientation + chip = torch.from_numpy(img).unsqueeze(0).to(device) + pred = model(chip) + pred = pred[pred[:, :, 4] > opt.conf_thres] + + if len(pred) > 0: + preds.append(pred.unsqueeze(0)) + + if len(preds) > 0: + detections = non_max_suppression(torch.cat(preds, 1), opt.conf_thres, opt.nms_thres) + img_detections.extend(detections) + imgs.extend(img_paths) + + print('Batch %d... (Done %.3fs)' % (batch_i, time.time() - prev_time)) + prev_time = time.time() + + # Bounding-box colors + color_list = [[random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)] for _ in range(len(classes))] + + if len(img_detections) == 0: + return + + # Iterate through images and save plot of detections + for img_i, (path, detections) in enumerate(zip(imgs, img_detections)): + print("image %g: '%s'" % (img_i, path)) + + if opt.plot_flag: + img = cv2.imread(path) + + # The amount of padding that was added + pad_x = max(img.shape[0] - img.shape[1], 0) * (opt.img_size / max(img.shape)) + pad_y = max(img.shape[1] - img.shape[0], 0) * (opt.img_size / max(img.shape)) + # Image height and width after padding is removed + unpad_h = opt.img_size - pad_y + unpad_w = opt.img_size - pad_x + + # Draw bounding boxes and labels of detections + if detections is not None: + unique_classes = detections[:, -1].cpu().unique() + bbox_colors = random.sample(color_list, len(unique_classes)) + + # write results to .txt file + results_img_path = os.path.join(opt.output_folder, path.split('/')[-1]) + results_txt_path = results_img_path + '.txt' + if os.path.isfile(results_txt_path): + os.remove(results_txt_path) + + for i in unique_classes: + n = (detections[:, -1].cpu() == i).sum() + print('%g %ss' % (n, classes[int(i)])) + + for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections: + # Rescale coordinates to original dimensions + box_h = ((y2 - y1) / unpad_h) * img.shape[0] + box_w = ((x2 - x1) / unpad_w) * img.shape[1] + y1 = (((y1 - pad_y // 2) / unpad_h) * img.shape[0]).round().item() + x1 = (((x1 - pad_x // 2) / unpad_w) * img.shape[1]).round().item() + x2 = (x1 + box_w).round().item() + y2 = (y1 + box_h).round().item() + x1, y1, x2, y2 = max(x1, 0), max(y1, 0), max(x2, 0), max(y2, 0) + + # write to file + if opt.txt_out: + with open(results_txt_path, 'a') as file: + file.write(('%g %g %g %g %g %g \n') % (x1, y1, x2, y2, cls_pred, cls_conf * conf)) + + if opt.plot_flag: + # Add the bbox to the plot + label = '%s %.2f' % (classes[int(cls_pred)], cls_conf) if cls_conf > 0.05 else None + color = bbox_colors[int(np.where(unique_classes == int(cls_pred))[0])] + plot_one_box([x1, y1, x2, y2], img, label=label, color=color, line_thickness=3) + + if opt.plot_flag: + # Save generated image with detections + cv2.imwrite(results_img_path.replace('.bmp', '.jpg').replace('.tif', '.jpg'), img) + + +if __name__ == '__main__': + torch.cuda.empty_cache() + detect(opt) diff --git a/models.py b/models.py new file mode 100755 index 00000000..def6f7dc --- /dev/null +++ b/models.py @@ -0,0 +1,336 @@ +from collections import defaultdict + +import torch.nn as nn + +from utils.utils import * +from utils.parse_config import * + + +def create_modules(module_defs): + """ + Constructs module list of layer blocks from module configuration in module_defs + """ + hyperparams = module_defs.pop(0) + output_filters = [int(hyperparams['channels'])] + module_list = nn.ModuleList() + for i, module_def in enumerate(module_defs): + modules = nn.Sequential() + + if module_def['type'] == 'convolutional': + bn = int(module_def['batch_normalize']) + filters = int(module_def['filters']) + kernel_size = int(module_def['size']) + pad = (kernel_size - 1) // 2 if int(module_def['pad']) else 0 + modules.add_module('conv_%d' % i, nn.Conv2d(in_channels=output_filters[-1], + out_channels=filters, + kernel_size=kernel_size, + stride=int(module_def['stride']), + padding=pad, + bias=not bn)) + if bn: + modules.add_module('batch_norm_%d' % i, nn.BatchNorm2d(filters)) + if module_def['activation'] == 'leaky': + modules.add_module('leaky_%d' % i, nn.LeakyReLU(0.1)) + + elif module_def['type'] == 'upsample': + upsample = nn.Upsample(scale_factor=int(module_def['stride']), mode='nearest') + modules.add_module('upsample_%d' % i, upsample) + + elif module_def['type'] == 'route': + layers = [int(x) for x in module_def["layers"].split(',')] + filters = sum([output_filters[layer_i] for layer_i in layers]) + modules.add_module('route_%d' % i, EmptyLayer()) + + elif module_def['type'] == 'shortcut': + filters = output_filters[int(module_def['from'])] + modules.add_module("shortcut_%d" % i, EmptyLayer()) + + elif module_def["type"] == "yolo": + anchor_idxs = [int(x) for x in module_def["mask"].split(",")] + # Extract anchors + anchors = [float(x) for x in module_def["anchors"].split(",")] + anchors = [(anchors[i], anchors[i + 1]) for i in range(0, len(anchors), 2)] + anchors = [anchors[i] for i in anchor_idxs] + num_classes = int(module_def['classes']) + img_height = int(hyperparams['height']) + # Define detection layer + yolo_layer = YOLOLayer(anchors, num_classes, img_height, anchor_idxs) + modules.add_module('yolo_%d' % i, yolo_layer) + + # Register module list and number of output filters + module_list.append(modules) + output_filters.append(filters) + + return hyperparams, module_list + + +class EmptyLayer(nn.Module): + """Placeholder for 'route' and 'shortcut' layers""" + + def __init__(self): + super(EmptyLayer, self).__init__() + + +class YOLOLayer(nn.Module): + # YOLO Layer 0 + + def __init__(self, anchors, nC, img_dim, anchor_idxs): + super(YOLOLayer, self).__init__() + + anchors = [(a_w, a_h) for a_w, a_h in anchors] # (pixels) + nA = len(anchors) + + self.anchors = anchors + self.nA = nA # number of anchors (3) + self.nC = nC # number of classes (60) + self.bbox_attrs = 5 + nC + self.img_dim = img_dim # from hyperparams in cfg file, NOT from parser + + if anchor_idxs[0] == (nA * 2): # 6 + stride = 32 + elif anchor_idxs[0] == nA: # 3 + stride = 16 + else: + stride = 8 + + # Build anchor grids + nG = int(self.img_dim / stride) + self.grid_x = torch.arange(nG).repeat(nG, 1).view([1, 1, nG, nG]).float() + self.grid_y = torch.arange(nG).repeat(nG, 1).t().view([1, 1, nG, nG]).float() + self.scaled_anchors = torch.FloatTensor([(a_w / stride, a_h / stride) for a_w, a_h in anchors]) + self.anchor_w = self.scaled_anchors[:, 0:1].view((1, nA, 1, 1)) + self.anchor_h = self.scaled_anchors[:, 1:2].view((1, nA, 1, 1)) + + def forward(self, p, targets=None, requestPrecision=False, epoch=None): + FT = torch.cuda.FloatTensor if p.is_cuda else torch.FloatTensor + # device = torch.device('cuda:0' if p.is_cuda else 'cpu') + + bs = p.shape[0] + nG = p.shape[2] + stride = self.img_dim / nG + + if p.is_cuda and not self.grid_x.is_cuda: + self.grid_x, self.grid_y = self.grid_x.cuda(), self.grid_y.cuda() + self.anchor_w, self.anchor_h = self.anchor_w.cuda(), self.anchor_h.cuda() + # self.scaled_anchors = self.scaled_anchors.cuda() + + # x.view(4, 650, 19, 19) -- > (4, 10, 19, 19, 65) # (bs, anchors, grid, grid, classes + xywh) + p = p.view(bs, self.nA, self.bbox_attrs, nG, nG).permute(0, 1, 3, 4, 2).contiguous() # prediction + + # Get outputs + x = torch.sigmoid(p[..., 0]) # Center x + y = torch.sigmoid(p[..., 1]) # Center y + w = p[..., 2] # Width + h = p[..., 3] # Height + width = torch.exp(w.data) * self.anchor_w + height = torch.exp(h.data) * self.anchor_h + + # Add offset and scale with anchors (in grid space, i.e. 0-13) + pred_boxes = FT(bs, self.nA, nG, nG, 4) + pred_conf = p[..., 4] # Conf + pred_cls = p[..., 5:] # Class + + # Training + if targets is not None: + BCEWithLogitsLoss1 = nn.BCEWithLogitsLoss(size_average=False) # version 0.4.0 + BCEWithLogitsLoss0 = nn.BCEWithLogitsLoss() + # BCEWithLogitsLoss2 = nn.BCEWithLogitsLoss(size_average=True) + MSELoss = nn.MSELoss(size_average=False) # version 0.4.0 + CrossEntropyLoss = nn.CrossEntropyLoss() + + if requestPrecision: + gx = self.grid_x[:, :, :nG, :nG] + gy = self.grid_y[:, :, :nG, :nG] + pred_boxes[..., 0] = x.data + gx - width / 2 + pred_boxes[..., 1] = y.data + gy - height / 2 + pred_boxes[..., 2] = x.data + gx + width / 2 + pred_boxes[..., 3] = y.data + gy + height / 2 + + tx, ty, tw, th, mask, tcls, TP, FP, FN, TC = \ + build_targets(pred_boxes, pred_conf, pred_cls, targets, self.scaled_anchors, self.nA, self.nC, nG, + requestPrecision) + tcls = tcls[mask] + if x.is_cuda: + tx, ty, tw, th, mask, tcls = tx.cuda(), ty.cuda(), tw.cuda(), th.cuda(), mask.cuda(), tcls.cuda() + + # Mask outputs to ignore non-existing objects (but keep confidence predictions) + nM = mask.sum().float() + nGT = sum([len(x) for x in targets]) + if nM > 0: + lx = 5 * MSELoss(x[mask], tx[mask]) + ly = 5 * MSELoss(y[mask], ty[mask]) + lw = 5 * MSELoss(w[mask], tw[mask]) + lh = 5 * MSELoss(h[mask], th[mask]) + lconf = 1.5 * BCEWithLogitsLoss1(pred_conf[mask], mask[mask].float()) + + lcls = nM * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1)) + # lcls = BCEWithLogitsLoss1(pred_cls[mask], tcls.float()) + else: + lx, ly, lw, lh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0]), FT([0]) + + lconf += nM * BCEWithLogitsLoss0(pred_conf[~mask], mask[~mask].float()) + + loss = lx + ly + lw + lh + lconf + lcls + i = torch.sigmoid(pred_conf[~mask]) > 0.99 + FPe = torch.zeros(self.nC) + if i.sum() > 0: + FP_classes = torch.argmax(pred_cls[~mask][i], 1) + for c in FP_classes: + FPe[c] += 1 + + return loss, loss.item(), lx.item(), ly.item(), lw.item(), lh.item(), lconf.item(), lcls.item(), \ + nGT, TP, FP, FPe, FN, TC + + else: + pred_boxes[..., 0] = x.data + self.grid_x + pred_boxes[..., 1] = y.data + self.grid_y + pred_boxes[..., 2] = width + pred_boxes[..., 3] = height + + # If not in training phase return predictions + output = torch.cat((pred_boxes.view(bs, -1, 4) * stride, + torch.sigmoid(pred_conf.view(bs, -1, 1)), pred_cls.view(bs, -1, self.nC)), -1) + return output.data + + +class Darknet(nn.Module): + """YOLOv3 object detection model""" + + def __init__(self, config_path, img_size=416): + super(Darknet, self).__init__() + self.module_defs = parse_model_config(config_path) + self.module_defs[0]['height'] = img_size + self.hyperparams, self.module_list = create_modules(self.module_defs) + self.img_size = img_size + self.loss_names = ['loss', 'x', 'y', 'w', 'h', 'conf', 'cls', 'nGT', 'TP', 'FP', 'FPe', 'FN', 'TC'] + + def forward(self, x, targets=None, requestPrecision=False, epoch=None): + is_training = targets is not None + output = [] + self.losses = defaultdict(float) + layer_outputs = [] + + for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)): + if module_def['type'] in ['convolutional', 'upsample']: + x = module(x) + elif module_def['type'] == 'route': + layer_i = [int(x) for x in module_def['layers'].split(',')] + x = torch.cat([layer_outputs[i] for i in layer_i], 1) + elif module_def['type'] == 'shortcut': + layer_i = int(module_def['from']) + x = layer_outputs[-1] + layer_outputs[layer_i] + elif module_def['type'] == 'yolo': + # Train phase: get loss + if is_training: + x, *losses = module[0](x, targets, requestPrecision, epoch) + for name, loss in zip(self.loss_names, losses): + self.losses[name] += loss + # Test phase: Get detections + else: + x = module(x) + output.append(x) + layer_outputs.append(x) + + if is_training: + self.losses['nGT'] /= 3 + self.losses['TC'] /= 3 + metrics = torch.zeros(4, len(self.losses['FPe'])) # TP, FP, FN, target_count + + ui = np.unique(self.losses['TC'])[1:] + for i in ui: + j = self.losses['TC'] == float(i) + metrics[0, i] = (self.losses['TP'][j] > 0).sum().float() # TP + metrics[1, i] = (self.losses['FP'][j] > 0).sum().float() # FP + metrics[2, i] = (self.losses['FN'][j] == 3).sum().float() # FN + metrics[3] = metrics.sum(0) + metrics[1] += self.losses['FPe'] + + self.losses['TP'] = metrics[0].sum() + self.losses['FP'] = metrics[1].sum() + self.losses['FN'] = metrics[2].sum() + self.losses['TC'] = 0 + self.losses['metrics'] = metrics + + return sum(output) if is_training else torch.cat(output, 1) + + +def load_weights(self, weights_path): + """Parses and loads the weights stored in 'weights_path'""" + + # Open the weights file + fp = open(weights_path, "rb") + header = np.fromfile(fp, dtype=np.int32, count=5) # First five are header values + + # Needed to write header when saving weights + self.header_info = header + + self.seen = header[3] + weights = np.fromfile(fp, dtype=np.float32) # The rest are weights + fp.close() + + ptr = 0 + for i, (module_def, module) in enumerate(zip(self.module_defs, self.module_list)): + if module_def['type'] == 'convolutional': + conv_layer = module[0] + if module_def['batch_normalize']: + # Load BN bias, weights, running mean and running variance + bn_layer = module[1] + num_b = bn_layer.bias.numel() # Number of biases + # Bias + bn_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.bias) + bn_layer.bias.data.copy_(bn_b) + ptr += num_b + # Weight + bn_w = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.weight) + bn_layer.weight.data.copy_(bn_w) + ptr += num_b + # Running Mean + bn_rm = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_mean) + bn_layer.running_mean.data.copy_(bn_rm) + ptr += num_b + # Running Var + bn_rv = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(bn_layer.running_var) + bn_layer.running_var.data.copy_(bn_rv) + ptr += num_b + else: + # Load conv. bias + num_b = conv_layer.bias.numel() + conv_b = torch.from_numpy(weights[ptr:ptr + num_b]).view_as(conv_layer.bias) + conv_layer.bias.data.copy_(conv_b) + ptr += num_b + # Load conv. weights + num_w = conv_layer.weight.numel() + conv_w = torch.from_numpy(weights[ptr:ptr + num_w]).view_as(conv_layer.weight) + conv_layer.weight.data.copy_(conv_w) + ptr += num_w + + +""" + @:param path - path of the new weights file + @:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved) +""" + + +def save_weights(self, path, cutoff=-1): + fp = open(path, 'wb') + self.header_info[3] = self.seen + self.header_info.tofile(fp) + + # Iterate through layers + for i, (module_def, module) in enumerate(zip(self.module_defs[:cutoff], self.module_list[:cutoff])): + if module_def['type'] == 'convolutional': + conv_layer = module[0] + # If batch norm, load bn first + if module_def['batch_normalize']: + bn_layer = module[1] + bn_layer.bias.data.cpu().numpy().tofile(fp) + bn_layer.weight.data.cpu().numpy().tofile(fp) + bn_layer.running_mean.data.cpu().numpy().tofile(fp) + bn_layer.running_var.data.cpu().numpy().tofile(fp) + # Load conv bias + else: + conv_layer.bias.data.cpu().numpy().tofile(fp) + # Load conv weights + conv_layer.weight.data.cpu().numpy().tofile(fp) + + fp.close() diff --git a/requirements.txt b/requirements.txt new file mode 100755 index 00000000..2e16f2e3 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,8 @@ +# pip3 install -U -r requirements.txt +numpy +scipy +opencv-python +torch +matplotlib +tqdm +h5py \ No newline at end of file diff --git a/results.txt b/results.txt new file mode 100644 index 00000000..07dd9bd0 --- /dev/null +++ b/results.txt @@ -0,0 +1,3928 @@ + 0/998 0/3375 129 127 517 687 492 1.27e+03 3.22e+03 0 0 149 0 0 149 3.8 + 0/998 1/3375 112 115 596 603 435 1.13e+03 2.99e+03 0 0 115 0 0 115 0.585 + 0/998 2/3375 93.7 95.5 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67 146 147 158 554 1.14e+03 0.000895 0.000138 89 3 15 84 0.617 + 0/998 1902/3375 67.7 67 146 147 158 554 1.14e+03 0.000895 0.000138 29 0 1 29 0.598 + 0/998 1903/3375 67.7 67 146 147 158 554 1.14e+03 0.000895 0.000138 105 0 0 105 0.603 + 0/998 1904/3375 67.7 67 146 147 158 554 1.14e+03 0.000895 0.000138 74 0 4 74 0.607 + 0/998 1905/3375 67.7 67 146 147 158 554 1.14e+03 0.000894 0.000138 115 0 17 114 0.596 + 0/998 1906/3375 67.7 67 146 147 158 554 1.14e+03 0.000894 0.000138 69 0 2 69 0.604 + 0/998 1907/3375 67.7 67 146 147 158 554 1.14e+03 0.000893 0.000138 104 0 20 102 0.599 + 0/998 1908/3375 67.7 67 146 147 158 554 1.14e+03 0.000894 0.000138 111 1 5 110 0.604 + 0/998 1909/3375 67.7 67 146 147 158 554 1.14e+03 0.000877 0.000138 73 0 11 68 0.61 + 0/998 1910/3375 67.7 67 146 147 158 554 1.14e+03 0.000912 0.000141 113 3 29 106 0.602 + 0/998 1911/3375 67.7 67 146 147 158 554 1.14e+03 0.000912 0.000141 87 0 3 87 0.602 + 0/998 1912/3375 67.7 67 146 147 158 554 1.14e+03 0.000912 0.000141 39 0 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158 553 1.14e+03 0.000926 0.000146 42 0 30 42 0.599 + 0/998 1925/3375 67.7 66.9 146 147 158 553 1.14e+03 0.000928 0.000146 96 2 19 94 0.6 + 0/998 1926/3375 67.7 66.9 146 147 158 553 1.14e+03 0.000924 0.000146 99 0 38 99 0.604 + 0/998 1927/3375 67.7 66.9 146 147 158 554 1.14e+03 0.000923 0.000146 76 0 5 75 0.588 + 0/998 1928/3375 67.7 66.9 146 147 158 553 1.14e+03 0.000923 0.000146 80 0 5 80 0.6 + 0/998 1929/3375 67.7 67 146 147 158 554 1.14e+03 0.000921 0.000146 134 0 22 132 0.622 + 0/998 1930/3375 67.7 67 146 147 158 554 1.14e+03 0.000922 0.000146 95 1 14 92 0.604 + 0/998 1931/3375 67.7 67 146 147 158 554 1.14e+03 0.00092 0.000146 131 0 21 129 0.616 + 0/998 1932/3375 67.7 67 146 147 158 554 1.14e+03 0.000914 0.000146 49 0 50 44 0.594 + 0/998 1933/3375 67.7 67 146 147 158 554 1.14e+03 0.00091 0.000146 78 1 68 72 0.631 + 0/998 1934/3375 67.7 67 146 147 158 554 1.14e+03 0.000911 0.000146 128 1 17 122 0.615 + 0/998 1935/3375 67.7 67 146 147 158 554 1.14e+03 0.000943 0.00015 141 6 68 131 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0.00015 55 0 4 55 0.6 + 0/998 1948/3375 67.7 67 146 147 158 553 1.14e+03 0.000918 0.00015 124 0 3 124 0.595 + 0/998 1949/3375 67.7 67 146 147 158 553 1.14e+03 0.000918 0.00015 91 1 21 85 0.594 + 0/998 1950/3375 67.7 67 146 147 158 553 1.14e+03 0.000919 0.00015 69 1 6 68 0.603 + 0/998 1951/3375 67.7 67 146 147 158 553 1.14e+03 0.000917 0.00015 75 0 13 75 0.592 + 0/998 1952/3375 67.7 67 146 147 157 553 1.14e+03 0.000914 0.00015 58 0 41 58 0.606 + 0/998 1953/3375 67.7 67 146 147 157 553 1.14e+03 0.000916 0.00015 82 2 15 79 0.599 + 0/998 1954/3375 67.7 67 146 147 157 553 1.14e+03 0.000915 0.00015 86 0 7 86 0.599 + 0/998 1955/3375 67.7 67 146 147 157 553 1.14e+03 0.000914 0.00015 88 0 11 88 0.609 + 0/998 1956/3375 67.7 67 146 147 157 553 1.14e+03 0.000907 0.00015 43 1 42 42 0.597 + 0/998 1957/3375 67.7 67 146 147 157 553 1.14e+03 0.000906 0.00015 125 0 12 124 0.602 + 0/998 1958/3375 67.7 67 146 147 157 553 1.14e+03 0.000906 0.00015 166 0 2 165 0.6 + 0/998 1959/3375 67.7 67 146 147 157 553 1.14e+03 0.000906 0.00015 70 0 14 69 0.618 + 0/998 1960/3375 67.7 67 146 147 157 553 1.14e+03 0.000905 0.00015 82 1 20 79 0.605 + 0/998 1961/3375 67.7 67 146 147 157 553 1.14e+03 0.000901 0.00015 75 0 33 73 0.616 + 0/998 1962/3375 67.7 67 146 147 157 553 1.14e+03 0.000898 0.00015 69 0 56 69 0.586 + 0/998 1963/3375 67.7 67 146 147 157 553 1.14e+03 0.000898 0.00015 137 0 5 137 0.626 + 0/998 1964/3375 67.7 67 146 147 157 553 1.14e+03 0.000897 0.00015 50 0 13 49 0.586 + 0/998 1965/3375 67.7 67 146 147 157 553 1.14e+03 0.000896 0.00015 104 1 54 100 0.613 + 0/998 1966/3375 67.7 67 146 147 157 553 1.14e+03 0.000896 0.00015 105 0 11 105 0.597 + 0/998 1967/3375 67.7 67 146 147 157 553 1.14e+03 0.000934 0.000154 102 3 41 98 0.594 + 0/998 1968/3375 67.7 67 146 147 157 553 1.14e+03 0.000931 0.000154 61 0 66 60 0.599 + 0/998 1969/3375 67.7 67 146 147 157 553 1.14e+03 0.00093 0.000155 152 4 92 141 0.608 + 0/998 1970/3375 67.7 67 146 147 157 553 1.14e+03 0.00092 0.000156 73 2 104 71 0.607 + 0/998 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0.00016 61 0 30 60 0.592 + 0/998 1983/3375 67.7 67 146 146 157 553 1.14e+03 0.000927 0.000161 55 1 9 54 0.598 + 0/998 1984/3375 67.7 67 146 146 157 553 1.14e+03 0.000927 0.000161 56 1 14 54 0.617 + 0/998 1985/3375 67.7 67 146 146 157 553 1.14e+03 0.000926 0.000161 105 0 29 102 0.619 + 0/998 1986/3375 67.7 67 146 146 157 553 1.14e+03 0.000925 0.000161 94 1 48 89 0.588 + 0/998 1987/3375 67.7 67 146 146 157 553 1.14e+03 0.000924 0.000161 89 0 28 89 0.61 + 0/998 1988/3375 67.7 67 146 146 157 553 1.14e+03 0.000927 0.000162 92 4 44 81 0.609 + 0/998 1989/3375 67.7 67 146 146 157 553 1.14e+03 0.000926 0.000162 91 1 48 85 0.6 + 0/998 1990/3375 67.7 67 146 146 157 553 1.14e+03 0.000926 0.000162 68 0 18 68 0.591 + 0/998 1991/3375 67.7 67 146 146 157 553 1.14e+03 0.000924 0.000162 83 0 50 78 0.598 + 0/998 1992/3375 67.6 66.9 146 146 157 553 1.14e+03 0.000924 0.000162 50 2 46 46 0.595 + 0/998 1993/3375 67.6 66.9 146 146 157 553 1.14e+03 0.000924 0.000162 84 0 7 84 0.595 + 0/998 1994/3375 67.6 66.9 146 146 157 553 1.14e+03 0.000923 0.000162 55 0 32 54 0.603 + 0/998 1995/3375 67.6 66.9 146 146 157 553 1.14e+03 0.000922 0.000162 62 0 19 60 0.601 + 0/998 1996/3375 67.6 66.9 146 146 157 552 1.14e+03 0.000918 0.000162 60 0 59 60 0.605 + 0/998 1997/3375 67.6 66.9 146 146 157 552 1.14e+03 0.00092 0.000163 82 4 80 77 0.607 + 0/998 1998/3375 67.6 66.9 146 146 157 552 1.14e+03 0.000919 0.000163 65 3 118 61 0.594 + 0/998 1999/3375 67.6 66.9 146 146 157 552 1.14e+03 0.000918 0.000163 52 0 26 52 0.588 + 0/998 2000/3375 67.6 66.9 146 146 157 552 1.14e+03 0.000914 0.000163 52 0 47 52 0.603 + 0/998 2001/3375 67.6 66.9 145 146 157 552 1.14e+03 0.000917 0.000165 98 7 123 85 0.606 + 0/998 2002/3375 67.6 66.9 146 146 157 552 1.14e+03 0.000917 0.000165 92 1 48 87 0.605 + 0/998 2003/3375 67.6 66.9 145 146 157 552 1.13e+03 0.000918 0.000166 65 2 38 63 0.614 + 0/998 2004/3375 67.6 66.9 146 146 157 552 1.14e+03 0.000918 0.000166 122 1 13 121 0.629 + 0/998 2005/3375 67.6 66.9 146 146 157 552 1.14e+03 0.000918 0.000166 103 0 4 103 0.6 + 0/998 2006/3375 67.6 66.9 146 146 157 552 1.14e+03 0.000917 0.000166 82 1 40 80 0.594 + 0/998 2007/3375 67.6 66.9 145 146 157 552 1.13e+03 0.000917 0.000166 48 0 12 48 0.603 + 0/998 2008/3375 67.6 66.9 145 146 157 552 1.13e+03 0.000916 0.000166 70 0 26 70 0.584 + 0/998 2009/3375 67.6 66.9 145 146 157 552 1.13e+03 0.000916 0.000166 71 1 22 69 0.61 + 0/998 2010/3375 67.6 66.9 145 146 157 552 1.13e+03 0.000915 0.000166 65 0 33 65 0.598 + 0/998 2011/3375 67.6 66.9 145 146 157 552 1.13e+03 0.000917 0.000166 94 2 26 87 0.607 + 0/998 2012/3375 67.6 66.9 145 146 157 552 1.13e+03 0.000916 0.000166 65 0 13 65 0.622 + 0/998 2013/3375 67.6 66.9 145 146 157 552 1.13e+03 0.000914 0.000166 68 0 50 68 0.608 + 0/998 2014/3375 67.6 66.9 145 146 157 552 1.13e+03 0.000914 0.000166 102 0 4 102 0.606 + 0/998 2015/3375 67.5 66.9 145 146 157 552 1.13e+03 0.000912 0.000166 46 0 42 45 0.602 + 0/998 2016/3375 67.5 66.8 145 146 157 552 1.13e+03 0.000914 0.000167 74 2 11 71 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66.8 145 146 156 551 1.13e+03 0.000901 0.000169 85 0 19 83 0.605 + 0/998 2029/3375 67.5 66.8 145 146 156 551 1.13e+03 0.000898 0.000169 47 0 65 47 0.605 + 0/998 2030/3375 67.5 66.8 145 145 156 551 1.13e+03 0.000896 0.000169 103 1 98 99 0.601 + 0/998 2031/3375 67.5 66.8 145 146 156 551 1.13e+03 0.000896 0.000169 103 2 59 96 0.605 + 0/998 2032/3375 67.5 66.8 145 146 156 551 1.13e+03 0.000896 0.00017 126 6 132 115 0.604 + 0/998 2033/3375 67.5 66.8 145 145 156 551 1.13e+03 0.000934 0.000178 84 16 333 64 0.611 + 0/998 2034/3375 67.5 66.8 145 145 156 551 1.13e+03 0.000927 0.000178 103 0 125 99 0.597 + 0/998 2035/3375 67.5 66.8 145 146 156 551 1.13e+03 0.0191 0.000188 127 4 41 119 0.599 + 0/998 2036/3375 67.5 66.8 145 146 156 551 1.13e+03 0.0191 0.000188 66 1 62 65 0.609 + 0/998 2037/3375 67.5 66.8 145 146 156 551 1.13e+03 0.0191 0.000188 123 1 30 119 0.598 + 0/998 2038/3375 67.5 66.8 145 146 156 551 1.13e+03 0.0191 0.000189 76 2 54 72 0.607 + 0/998 2039/3375 67.5 66.8 145 146 156 551 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439/3375 59.9 59.8 113 110 115 450 907 0.00521 0.004 91 2 53 87 0.604 + 1/998 440/3375 59.9 59.8 113 110 114 449 907 0.00521 0.00399 55 1 93 48 0.611 + 1/998 441/3375 60 59.9 113 110 115 450 907 0.0052 0.00399 136 5 56 128 0.601 + 1/998 442/3375 60 59.8 113 110 115 450 907 0.0052 0.00397 63 6 113 56 0.605 + 1/998 443/3375 60 59.8 113 110 115 450 907 0.0052 0.00396 85 2 63 78 0.612 + 1/998 444/3375 60 59.8 113 110 114 450 907 0.00524 0.00399 68 1 71 64 0.59 + 1/998 445/3375 59.9 59.8 113 110 114 449 906 0.00524 0.00399 50 1 121 43 0.599 + 1/998 446/3375 60 59.8 113 110 114 450 907 0.00523 0.00399 109 1 116 105 0.606 + 1/998 447/3375 59.9 59.8 113 110 114 450 907 0.00524 0.00399 76 9 83 62 0.678 + 1/998 448/3375 59.9 59.8 113 110 114 449 906 0.00523 0.00399 58 5 119 49 0.608 + 1/998 449/3375 59.9 59.8 113 110 114 450 907 0.00522 0.00399 81 2 135 77 0.607 + 1/998 450/3375 59.9 59.7 113 110 114 449 906 0.00522 0.00399 78 1 59 76 0.614 + 1/998 451/3375 59.9 59.8 113 110 114 449 906 0.00522 0.00397 88 4 114 83 0.591 + 1/998 452/3375 59.9 59.8 113 110 114 450 907 0.00521 0.00396 97 1 145 87 0.614 + 1/998 453/3375 60 59.8 113 110 115 450 908 0.00521 0.00394 125 2 61 122 0.6 + 1/998 454/3375 60 59.8 113 110 114 450 907 0.0052 0.00393 63 3 179 58 0.604 + 1/998 455/3375 60 59.8 113 110 114 450 908 0.00519 0.00392 95 1 52 89 0.596 + 1/998 456/3375 60 59.8 113 110 114 450 907 0.0052 0.00396 89 19 384 59 0.626 + 1/998 457/3375 60 59.8 113 110 114 450 907 0.0052 0.00396 79 4 101 74 0.621 + 1/998 458/3375 60 59.8 113 110 115 450 908 0.00512 0.00395 92 2 78 84 0.603 + 1/998 459/3375 60 59.8 113 110 115 450 907 0.00512 0.00395 61 9 277 44 0.594 + 1/998 460/3375 60 59.9 113 110 115 450 907 0.00512 0.00396 106 7 112 96 0.604 + 1/998 461/3375 59.9 59.9 113 110 114 449 907 0.00511 0.00395 47 2 66 41 0.601 + 1/998 462/3375 59.9 59.8 113 110 114 449 906 0.00511 0.00395 53 3 131 47 0.597 + 1/998 463/3375 59.8 59.8 113 110 114 449 905 0.00511 0.00397 58 3 288 46 0.605 + 1/998 464/3375 59.8 59.8 113 110 114 449 905 0.00509 0.00397 83 1 94 77 0.594 + 1/998 465/3375 59.8 59.7 113 110 114 449 905 0.00509 0.00398 77 7 358 58 0.605 + 1/998 466/3375 59.8 59.8 113 110 114 449 905 0.00509 0.00398 94 2 49 84 0.615 + 1/998 467/3375 59.8 59.7 113 110 114 448 904 0.00509 0.00398 36 3 104 32 0.601 + 1/998 468/3375 59.8 59.7 113 110 114 448 905 0.00509 0.00397 97 2 65 92 0.606 + 1/998 469/3375 59.8 59.7 113 110 114 448 904 0.00507 0.00397 58 3 162 49 0.63 + 1/998 470/3375 59.8 59.7 113 110 114 448 904 0.00507 0.00397 78 4 77 73 0.597 + 1/998 471/3375 59.7 59.7 113 110 114 448 904 0.00507 0.00397 65 0 90 63 0.61 + 1/998 472/3375 59.7 59.6 113 110 114 448 903 0.00507 0.00397 79 7 192 62 0.619 + 1/998 473/3375 59.7 59.6 113 110 114 447 903 0.00506 0.00397 59 5 181 50 0.601 + 1/998 474/3375 59.7 59.6 112 110 114 447 902 0.00506 0.00398 68 9 191 53 0.593 + 1/998 475/3375 59.7 59.6 112 110 114 447 902 0.00507 0.004 87 8 112 78 0.6 + 1/998 476/3375 59.6 59.5 112 109 114 447 902 0.00504 0.004 44 2 45 42 0.595 + 1/998 477/3375 59.6 59.5 112 109 114 446 901 0.005 0.00399 58 1 49 52 0.595 + 1/998 478/3375 59.6 59.5 112 109 114 446 901 0.005 0.00399 88 1 17 84 0.598 + 1/998 479/3375 59.7 59.6 112 110 114 447 902 0.005 0.00398 150 3 66 140 0.613 + 1/998 480/3375 59.7 59.6 112 110 114 447 903 0.00497 0.00397 96 2 80 85 0.614 + 1/998 481/3375 59.7 59.7 112 110 114 447 902 0.00497 0.00398 83 14 295 62 0.609 + 1/998 482/3375 59.7 59.7 112 110 114 447 902 0.00495 0.00398 67 5 81 58 0.601 + 1/998 483/3375 59.7 59.7 112 110 114 447 902 0.00495 0.00397 84 5 77 74 0.606 + 1/998 484/3375 59.7 59.7 112 110 114 447 902 0.00494 0.00397 71 1 32 69 0.594 + 1/998 485/3375 59.7 59.6 112 110 114 447 901 0.00494 0.00397 51 8 184 40 0.593 + 1/998 486/3375 59.9 59.8 112 110 114 448 903 0.00494 0.00396 154 2 32 148 0.609 + 1/998 487/3375 59.8 59.7 112 110 114 447 903 0.00494 0.00396 66 5 111 57 0.592 + 1/998 488/3375 59.8 59.7 112 110 114 447 902 0.00494 0.00397 73 6 125 58 0.604 + 1/998 489/3375 59.8 59.7 112 110 114 447 902 0.00494 0.00396 45 0 126 43 0.58 + 1/998 490/3375 59.8 59.6 112 110 114 446 901 0.00494 0.00396 60 3 34 56 0.596 + 1/998 491/3375 59.8 59.7 112 110 114 447 902 0.00494 0.00395 139 1 31 135 0.595 + 1/998 492/3375 59.8 59.7 112 110 114 447 902 0.00493 0.00395 84 3 85 79 0.582 + 1/998 493/3375 59.9 59.7 112 110 114 447 902 0.00493 0.00395 96 5 75 88 0.598 + 1/998 494/3375 59.8 59.7 112 110 114 446 902 0.00493 0.00396 73 5 150 53 0.62 + 1/998 495/3375 59.8 59.6 112 110 114 446 901 0.00492 0.00396 91 8 149 78 0.604 + 1/998 496/3375 59.8 59.6 112 110 114 446 901 0.00492 0.00395 56 1 114 54 0.599 + 1/998 497/3375 59.8 59.6 112 110 114 446 901 0.00492 0.00395 90 4 122 83 0.601 + 1/998 498/3375 59.8 59.6 112 110 114 446 901 0.00492 0.00395 86 2 73 82 0.615 + 1/998 499/3375 59.9 59.6 112 110 114 446 901 0.00492 0.00394 109 4 91 100 0.636 + 1/998 500/3375 59.8 59.6 112 110 114 446 901 0.00491 0.00394 46 3 121 40 0.607 + 1/998 501/3375 59.8 59.6 112 110 114 446 901 0.00491 0.00395 92 4 158 83 0.612 + 1/998 502/3375 59.9 59.6 112 110 114 446 902 0.00491 0.00395 121 7 106 111 0.603 + 1/998 503/3375 59.9 59.6 112 110 114 446 902 0.0049 0.00395 76 1 147 75 0.613 + 1/998 504/3375 59.9 59.6 112 110 114 446 902 0.0049 0.00394 73 3 181 69 0.602 + 1/998 505/3375 59.9 59.6 112 110 114 446 902 0.00491 0.00397 90 11 135 75 0.612 + 1/998 506/3375 59.9 59.7 112 110 114 447 902 0.00491 0.00396 120 3 63 111 0.62 + 1/998 507/3375 59.9 59.7 112 110 114 447 902 0.00491 0.00396 61 2 113 54 0.606 + 1/998 508/3375 59.9 59.6 112 110 114 446 902 0.00491 0.00397 71 2 42 69 0.596 + 1/998 509/3375 59.9 59.6 112 110 114 446 902 0.00491 0.00397 70 3 156 62 0.608 + 1/998 510/3375 59.9 59.6 112 110 114 446 902 0.00489 0.00398 82 6 131 66 0.626 + 1/998 511/3375 59.9 59.7 112 110 114 447 902 0.00489 0.00398 93 4 98 85 0.651 + 1/998 512/3375 59.9 59.6 112 110 114 447 902 0.0049 0.00399 74 5 109 67 0.587 + 1/998 513/3375 60 59.7 112 110 114 446 902 0.0049 0.00397 97 3 81 90 0.602 + 1/998 514/3375 59.9 59.6 112 110 114 446 901 0.00489 0.00397 64 1 90 62 0.609 + 1/998 515/3375 60 59.7 112 110 114 447 902 0.00489 0.00396 114 0 83 113 0.613 + 1/998 516/3375 60 59.7 112 110 114 447 903 0.00489 0.00395 72 1 114 67 0.619 + 1/998 517/3375 59.9 59.7 112 110 114 447 902 0.00489 0.00395 59 2 82 54 0.611 + 1/998 518/3375 60 59.7 112 110 114 447 902 0.00488 0.00394 79 0 93 76 0.614 + 1/998 519/3375 60.1 59.8 112 110 114 447 903 0.00496 0.00397 134 4 36 125 0.614 + 1/998 520/3375 60 59.8 112 110 114 447 903 0.00496 0.00396 64 1 56 63 0.575 + 1/998 521/3375 60 59.7 112 110 114 447 902 0.00497 0.00397 52 3 60 47 0.588 + 1/998 522/3375 59.9 59.6 112 110 114 446 901 0.00496 0.00397 37 0 63 35 0.604 + 1/998 523/3375 59.9 59.6 112 110 114 446 901 0.00495 0.00397 60 2 164 54 0.612 + 1/998 524/3375 59.9 59.7 112 110 114 446 901 0.00495 0.00398 100 9 88 84 0.608 + 1/998 525/3375 59.9 59.7 112 109 114 447 902 0.00474 0.00397 87 1 73 83 0.624 + 1/998 526/3375 59.9 59.7 112 110 114 447 901 0.00479 0.004 80 5 87 70 0.613 + 1/998 527/3375 60 59.7 112 110 114 447 902 0.00478 0.004 103 2 73 99 0.606 + 1/998 528/3375 59.9 59.7 112 110 114 447 902 0.00478 0.00399 85 3 86 75 0.598 + 1/998 529/3375 60 59.7 112 110 114 447 903 0.00478 0.004 100 8 53 92 0.599 + 1/998 530/3375 60 59.8 112 110 114 448 904 0.00478 0.00399 124 1 24 121 0.601 + 1/998 531/3375 60.1 59.8 112 110 114 448 904 0.00477 0.00398 73 0 63 70 0.601 + 1/998 532/3375 60 59.8 112 110 114 448 903 0.00475 0.00398 69 0 130 64 0.629 + 1/998 533/3375 60 59.7 112 110 114 447 903 0.00474 0.00397 56 0 57 55 0.606 + 1/998 534/3375 60 59.7 112 110 114 448 903 0.00473 0.00397 98 0 45 92 0.616 + 1/998 535/3375 60 59.7 112 110 114 447 903 0.00473 0.00397 37 1 29 35 0.588 + 1/998 536/3375 60 59.7 112 110 114 448 903 0.00472 0.00397 92 8 207 72 0.607 + 1/998 537/3375 60 59.7 112 110 114 448 903 0.00472 0.00396 92 4 49 85 0.607 + 1/998 538/3375 60 59.7 112 110 114 448 903 0.00469 0.00396 91 1 113 86 0.611 + 1/998 539/3375 60 59.7 112 110 114 447 903 0.00469 0.00396 53 5 60 47 0.606 + 1/998 540/3375 60 59.7 112 110 114 448 903 0.00469 0.00395 88 1 77 85 0.601 + 0/998 0/416 90.9 90 596 585 367 939 2.67e+03 0 0 117 0 0 117 4.28 + 0/998 1/416 80.5 88.3 593 591 318 825 2.5e+03 0 0 87 0 0 87 0.579 + 0/998 2/416 74.3 82.9 500 502 297 763 2.22e+03 0 0 68 0 0 68 0.554 + 0/998 3/416 69.2 76.6 447 451 271 700 2.01e+03 0 0 59 0 0 59 0.553 + 0/998 4/416 65.6 69.9 395 403 253 652 1.84e+03 0 0 49 0 0 49 0.533 + 0/998 5/416 65.6 69.3 390 390 255 664 1.83e+03 0 0 97 0 0 97 0.531 + 0/998 6/416 62.9 67.2 374 364 245 636 1.75e+03 0 0 50 0 0 50 0.545 + 0/998 7/416 65.9 69.6 377 369 254 662 1.8e+03 0 0 97 0 0 97 0.539 + 0/998 8/416 65.3 67.7 367 353 250 652 1.75e+03 0 0 64 0 3 64 0.549 + 0/998 9/416 65.5 68.6 359 357 254 663 1.77e+03 0 0 81 0 14 81 0.541 + 0/998 10/416 64.6 66.9 354 349 250 650 1.73e+03 0 0 54 0 2 54 0.537 diff --git a/test.py b/test.py new file mode 100644 index 00000000..88054f70 --- /dev/null +++ b/test.py @@ -0,0 +1,130 @@ +import argparse + +from models import * +from utils.datasets import * +from utils.utils import * + +parser = argparse.ArgumentParser() +parser.add_argument('--epochs', type=int, default=200, help='number of epochs') +parser.add_argument('--batch_size', type=int, default=32, help='size of each image batch') +parser.add_argument('--model_config_path', type=str, default='cfg/yolov3.cfg', help='path to model config file') +parser.add_argument('--data_config_path', type=str, default='cfg/coco.data', help='path to data config file') +parser.add_argument('--weights_path', type=str, default='checkpoints/yolov3.weights', help='path to weights file') +parser.add_argument('--class_path', type=str, default='data/coco.names', help='path to class label file') +parser.add_argument('--iou_thres', type=float, default=0.5, help='iou threshold required to qualify as detected') +parser.add_argument('--conf_thres', type=float, default=0.5, help='object confidence threshold') +parser.add_argument('--nms_thres', type=float, default=0.45, help='iou threshold for non-maximum suppression') +parser.add_argument('--n_cpu', type=int, default=0, help='number of cpu threads to use during batch generation') +parser.add_argument('--img_size', type=int, default=416, help='size of each image dimension') +parser.add_argument('--use_cuda', type=bool, default=True, help='whether to use cuda if available') +opt = parser.parse_args() +print(opt) + +cuda = torch.cuda.is_available() and opt.use_cuda +device = torch.device('cuda:0' if cuda else 'cpu') + +# Get data configuration +data_config = parse_data_config(opt.data_config_path) +test_path = data_config['valid'] +num_classes = int(data_config['classes']) + +# Initiate model +model = Darknet(opt.model_config_path, opt.img_size) + +# Load weights +weights_path = 'checkpoints/yolov3.pt' +if weights_path.endswith('.weights'): # darknet format + load_weights(model, weights_path) +elif weights_path.endswith('.pt'): # pytorch format + checkpoint = torch.load(weights_path, map_location='cpu') + model.load_state_dict(checkpoint['model']) + del checkpoint + +model.to(device).eval() + +# Get dataloader +# dataset = ListDataset(test_path) +# dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=False, num_workers=opt.n_cpu) +dataloader = ListDataset(test_path, batch_size=opt.batch_size, img_size=opt.img_size) + +Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor + +n_gt = 0 +correct = 0 + +print('Compute mAP...') + +outputs = [] +targets = None +APs = [] +for batch_i, (imgs, targets) in enumerate(dataloader): + imgs = imgs.to(device) + + with torch.no_grad(): + output = model(imgs) + output = non_max_suppression(output, conf_thres=opt.conf_thres, nms_thres=opt.nms_thres) + + # Compute average precision for each sample + for sample_i in range(len(targets)): + correct = [] + + # Get labels for sample where width is not zero (dummies) + annotations = targets[sample_i] + # Extract detections + detections = output[sample_i] + + if detections is None: + # If there are no detections but there are annotations mask as zero AP + if annotations.size(0) != 0: + APs.append(0) + continue + + # Get detections sorted by decreasing confidence scores + detections = detections[np.argsort(-detections[:, 4])] + + # If no annotations add number of detections as incorrect + if annotations.size(0) == 0: + correct.extend([0 for _ in range(len(detections))]) + else: + # Extract target boxes as (x1, y1, x2, y2) + target_boxes = torch.FloatTensor(annotations[:, 1:].shape) + target_boxes[:, 0] = (annotations[:, 1] - annotations[:, 3] / 2) + target_boxes[:, 1] = (annotations[:, 2] - annotations[:, 4] / 2) + target_boxes[:, 2] = (annotations[:, 1] + annotations[:, 3] / 2) + target_boxes[:, 3] = (annotations[:, 2] + annotations[:, 4] / 2) + target_boxes *= opt.img_size + + detected = [] + for *pred_bbox, conf, obj_conf, obj_pred in detections: + + pred_bbox = torch.FloatTensor(pred_bbox).view(1, -1) + # Compute iou with target boxes + iou = bbox_iou(pred_bbox, target_boxes) + # Extract index of largest overlap + best_i = np.argmax(iou) + # If overlap exceeds threshold and classification is correct mark as correct + if iou[best_i] > opt.iou_thres and obj_pred == annotations[best_i, 0] and best_i not in detected: + correct.append(1) + detected.append(best_i) + else: + correct.append(0) + + # Extract true and false positives + true_positives = np.array(correct) + false_positives = 1 - true_positives + + # Compute cumulative false positives and true positives + false_positives = np.cumsum(false_positives) + true_positives = np.cumsum(true_positives) + + # Compute recall and precision at all ranks + recall = true_positives / annotations.size(0) if annotations.size(0) else true_positives + precision = true_positives / np.maximum(true_positives + false_positives, np.finfo(np.float64).eps) + + # Compute average precision + AP = compute_ap(recall, precision) + APs.append(AP) + + print("+ Sample [%d/%d] AP: %.4f (%.4f)" % (len(APs), len(dataloader) * opt.batch_size, AP, np.mean(APs))) + +print("Mean Average Precision: %.4f" % np.mean(APs)) diff --git a/train.py b/train.py new file mode 100644 index 00000000..56435dcc --- /dev/null +++ b/train.py @@ -0,0 +1,192 @@ +import argparse +import time +from sys import platform + +from models import * +from utils.datasets import * +from utils.utils import * + +parser = argparse.ArgumentParser() +parser.add_argument('-epochs', type=int, default=999, help='number of epochs') +parser.add_argument('-batch_size', type=int, default=12, help='size of each image batch') +parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='data config file path') +parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path') +parser.add_argument('-img_size', type=int, default=32 * 13, help='size of each image dimension') +parser.add_argument('-resume', default=False, help='resume training flag') +opt = parser.parse_args() +print(opt) + +cuda = torch.cuda.is_available() +device = torch.device('cuda:0' if cuda else 'cpu') + +random.seed(0) +np.random.seed(0) +torch.manual_seed(0) +if cuda: + torch.cuda.manual_seed(0) + torch.cuda.manual_seed_all(0) + torch.backends.cudnn.benchmark = True + +def main(opt): + os.makedirs('checkpoints', exist_ok=True) + + # Configure run + data_config = parse_data_config(opt.data_config_path) + num_classes = int(data_config['classes']) + if platform == 'darwin': # macos + train_path = data_config['valid'] + else: # linux (gcp cloud) + train_path = '../coco/trainvalno5k.txt' + + # Initialize model + model = Darknet(opt.cfg, opt.img_size) + + # Get dataloader + dataloader = ListDataset(train_path, batch_size=opt.batch_size, img_size=opt.img_size) + + # reload saved optimizer state + start_epoch = 0 + best_loss = float('inf') + if opt.resume: + checkpoint = torch.load('checkpoints/latest.pt', map_location='cpu') + + model.load_state_dict(checkpoint['model']) + if torch.cuda.device_count() > 1: + print('Using ', torch.cuda.device_count(), ' GPUs') + model = nn.DataParallel(model) + model.to(device).train() + + # # Transfer learning + # for i, (name, p) in enumerate(model.named_parameters()): + # #name = name.replace('module_list.', '') + # #print('%4g %70s %9s %12g %20s %12g %12g' % ( + # # i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) + # if p.shape[0] != 650: # not YOLO layer + # p.requires_grad = False + + # Set optimizer + # optimizer = torch.optim.SGD(model.parameters(), lr=.001, momentum=.9, weight_decay=0.0005 * 0, nesterov=True) + # optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters())) + optimizer = torch.optim.Adam(model.parameters()) + optimizer.load_state_dict(checkpoint['optimizer']) + + start_epoch = checkpoint['epoch'] + 1 + best_loss = checkpoint['best_loss'] + + del checkpoint # current, saved + else: + if torch.cuda.device_count() > 1: + print('Using ', torch.cuda.device_count(), ' GPUs') + model = nn.DataParallel(model) + model.to(device).train() + optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-4, weight_decay=5e-4) + + # Set scheduler + # scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 24, eta_min=0.00001, last_epoch=-1) + # y = 0.001 * exp(-0.00921 * x) # 1e-4 @ 250, 1e-5 @ 500 + # scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.99082, last_epoch=start_epoch - 1) + + modelinfo(model) + t0, t1 = time.time(), time.time() + print('%10s' * 16 % ( + 'Epoch', 'Batch', 'x', 'y', 'w', 'h', 'conf', 'cls', 'total', 'P', 'R', 'nGT', 'TP', 'FP', 'FN', 'time')) + for epoch in range(opt.epochs): + epoch += start_epoch + + # img_size = random.choice([19, 20, 21, 22, 23, 24, 25]) * 32 + # dataloader = ListDataset(train_path, batch_size=opt.batch_size, img_size=img_size, targets_path=targets_path) + # print('Running image size %g' % img_size) + + # Update scheduler + # if epoch % 25 == 0: + # scheduler.last_epoch = -1 # for cosine annealing, restart every 25 epochs + # scheduler.step() + # if epoch <= 100: + # for g in optimizer.param_groups: + # g['lr'] = 0.0005 * (0.992 ** epoch) # 1/10 th every 250 epochs + # g['lr'] = 0.001 * (0.9773 ** epoch) # 1/10 th every 100 epochs + # g['lr'] = 0.0005 * (0.955 ** epoch) # 1/10 th every 50 epochs + # g['lr'] = 0.0005 * (0.926 ** epoch) # 1/10 th every 30 epochs + + ui = -1 + rloss = defaultdict(float) # running loss + metrics = torch.zeros(4, num_classes) + for i, (imgs, targets) in enumerate(dataloader): + + n = opt.batch_size # number of pictures at a time + for j in range(int(len(imgs) / n)): + targets_j = targets[j * n:j * n + n] + nGT = sum([len(x) for x in targets_j]) + if nGT < 1: + continue + + loss = model(imgs[j * n:j * n + n].to(device), targets_j, requestPrecision=True, epoch=epoch) + optimizer.zero_grad() + loss.backward() + optimizer.step() + + ui += 1 + metrics += model.losses['metrics'] + for key, val in model.losses.items(): + rloss[key] = (rloss[key] * ui + val) / (ui + 1) + + # Precision + precision = metrics[0] / (metrics[0] + metrics[1] + 1e-16) + k = (metrics[0] + metrics[1]) > 0 + if k.sum() > 0: + mean_precision = precision[k].mean() + else: + mean_precision = 0 + + # Recall + recall = metrics[0] / (metrics[0] + metrics[2] + 1e-16) + k = (metrics[0] + metrics[2]) > 0 + if k.sum() > 0: + mean_recall = recall[k].mean() + else: + mean_recall = 0 + + s = ('%10s%10s' + '%10.3g' * 14) % ( + '%g/%g' % (epoch, opt.epochs - 1), '%g/%g' % (i, len(dataloader) - 1), rloss['x'], + rloss['y'], rloss['w'], rloss['h'], rloss['conf'], rloss['cls'], + rloss['loss'], mean_precision, mean_recall, model.losses['nGT'], model.losses['TP'], + model.losses['FP'], model.losses['FN'], time.time() - t1) + t1 = time.time() + print(s) + + # if i == 1: + # return + + # Write epoch results + with open('results.txt', 'a') as file: + file.write(s + '\n') + + # Update best loss + loss_per_target = rloss['loss'] / rloss['nGT'] + if loss_per_target < best_loss: + best_loss = loss_per_target + + # Save latest checkpoint + checkpoint = {'epoch': epoch, + 'best_loss': best_loss, + 'model': model.state_dict(), + 'optimizer': optimizer.state_dict()} + torch.save(checkpoint, 'checkpoints/latest.pt') + + # Save best checkpoint + if best_loss == loss_per_target: + os.system('cp checkpoints/latest.pt checkpoints/best.pt') + + # Save backup checkpoint + if (epoch > 0) & (epoch % 100 == 0): + os.system('cp checkpoints/latest.pt checkpoints/backup' + str(epoch) + '.pt') + + # Save final model + dt = time.time() - t0 + print('Finished %g epochs in %.2fs (%.2fs/epoch)' % (epoch, dt, dt / (epoch + 1))) + + +if __name__ == '__main__': + torch.cuda.empty_cache() + main(opt) + torch.cuda.empty_cache() diff --git a/utils/datasets.py b/utils/datasets.py new file mode 100755 index 00000000..0760d7e5 --- /dev/null +++ b/utils/datasets.py @@ -0,0 +1,284 @@ +import glob +import math +import os +import random + +import cv2 +import numpy as np +import torch + +# from torch.utils.data import Dataset +from utils.utils import xyxy2xywh + + +class ImageFolder(): # for eval-only + def __init__(self, path, batch_size=1, img_size=416): + if os.path.isdir(path): + self.files = sorted(glob.glob('%s/*.*' % path)) + elif os.path.isfile(path): + self.files = [path] + + self.nF = len(self.files) # number of image files + self.nB = math.ceil(self.nF / batch_size) # number of batches + self.batch_size = batch_size + self.height = img_size + assert self.nF > 0, 'No images found in path %s' % path + + # RGB normalization values + # self.rgb_mean = np.array([60.134, 49.697, 40.746], dtype=np.float32).reshape((3, 1, 1)) + # self.rgb_std = np.array([29.99, 24.498, 22.046], dtype=np.float32).reshape((3, 1, 1)) + + def __iter__(self): + self.count = -1 + return self + + def __next__(self): + self.count += 1 + if self.count == self.nB: + raise StopIteration + img_path = self.files[self.count] + + # Read image + img = cv2.imread(img_path) # BGR + + # Padded resize + img, _, _, _ = resize_square(img, height=self.height, color=(127.5, 127.5, 127.5)) + + # Normalize RGB + img = img[:, :, ::-1].transpose(2, 0, 1) + img = np.ascontiguousarray(img, dtype=np.float32) + # img -= self.rgb_mean + # img /= self.rgb_std + img /= 255.0 + + return [img_path], img + + def __len__(self): + return self.nB # number of batches + + +class ListDataset(): # for training + def __init__(self, path, batch_size=1, img_size=608): + self.path = path + #self.img_files = sorted(glob.glob('%s/*.*' % path)) + with open(path, 'r') as file: + self.img_files = file.readlines() + self.img_files = [path.replace('\n', '').replace('/images','/Users/glennjocher/Downloads/DATA/coco/images') for path in self.img_files] + + self.label_files = [path.replace('images', 'labels').replace('.png', '.txt').replace('.jpg', '.txt') for path in + self.img_files] + + self.nF = len(self.img_files) # number of image files + self.nB = math.ceil(self.nF / batch_size) # number of batches + self.batch_size = batch_size + + #assert self.nB > 0, 'No images found in path %s' % path + self.height = img_size + + # RGB normalization values + # self.rgb_mean = np.array([60.134, 49.697, 40.746], dtype=np.float32).reshape((1, 3, 1, 1)) + # self.rgb_std = np.array([29.99, 24.498, 22.046], dtype=np.float32).reshape((1, 3, 1, 1)) + + def __iter__(self): + self.count = -1 + # self.shuffled_vector = np.random.permutation(self.nF) # shuffled vector + self.shuffled_vector = np.arange(self.nF) + return self + + def __next__(self): + self.count += 1 + if self.count == self.nB: + raise StopIteration + + ia = self.count * self.batch_size + ib = min((self.count + 1) * self.batch_size, self.nF) + + height = self.height + + img_all = [] + labels_all = [] + for index, files_index in enumerate(range(ia, ib)): + img_path = self.img_files[self.shuffled_vector[files_index]] + label_path = self.label_files[self.shuffled_vector[files_index]] + + img = cv2.imread(img_path) # BGR + if img is None: + continue + + augment_hsv = False + if augment_hsv: + # SV augmentation by 50% + fraction = 0.50 + img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) + S = img_hsv[:, :, 1].astype(np.float32) + V = img_hsv[:, :, 2].astype(np.float32) + + a = (random.random() * 2 - 1) * fraction + 1 + S *= a + if a > 1: + np.clip(S, a_min=0, a_max=255, out=S) + + a = (random.random() * 2 - 1) * fraction + 1 + V *= a + if a > 1: + np.clip(V, a_min=0, a_max=255, out=V) + + img_hsv[:, :, 1] = S.astype(np.uint8) + img_hsv[:, :, 2] = V.astype(np.uint8) + cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) + + h, w, _ = img.shape + img, ratio, padw, padh = resize_square(img, height=height, color=(127.5, 127.5, 127.5)) + + # Load labels + if os.path.isfile(label_path): + labels0 = np.loadtxt(label_path, dtype=np.float32).reshape(-1, 5) + + # Normalized xywh to pixel xyxy format + labels = labels0.copy() + labels[:, 1] = ratio * w * (labels0[:, 1] - labels0[:, 3] / 2) + padw + labels[:, 2] = ratio * h * (labels0[:, 2] - labels0[:, 4] / 2) + padh + labels[:, 3] = ratio * w * (labels0[:, 1] + labels0[:, 3] / 2) + padw + labels[:, 4] = ratio * h * (labels0[:, 2] + labels0[:, 4] / 2) + padh + else: + labels = np.array([]) + + # Augment image and labels + # img, labels, M = random_affine(img, targets=labels, degrees=(-5, 5), translate=(0.1, 0.1), scale=(0.8, 1.2)) # RGB + + plotFlag = False + if plotFlag: + import matplotlib.pyplot as plt + plt.subplot(4, 4, index + 1).imshow(img[:, :, ::-1]) + plt.plot(labels[:, [1, 3, 3, 1, 1]].T, labels[:, [2, 2, 4, 4, 2]].T, '.-') + + nL = len(labels) + if nL > 0: + # convert xyxy to xywh + labels[:, 1:5] = xyxy2xywh(labels[:, 1:5].copy()) / height + + # random left-right flip + lr_flip = False + if lr_flip & (random.random() > 0.5): + img = np.fliplr(img) + if nL > 0: + labels[:, 1] = 1 - labels[:, 1] + + # random up-down flip + ud_flip = False + if ud_flip & (random.random() > 0.5): + img = np.flipud(img) + if nL > 0: + labels[:, 2] = 1 - labels[:, 2] + + img_all.append(img) + labels_all.append(torch.from_numpy(labels)) + + # Normalize + img_all = np.stack(img_all)[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB and cv2 to pytorch + img_all = np.ascontiguousarray(img_all, dtype=np.float32) + # img_all -= self.rgb_mean + # img_all /= self.rgb_std + img_all /= 255.0 + + return torch.from_numpy(img_all), labels_all + + def __len__(self): + return self.nB # number of batches + + +def resize_square(img, height=416, color=(0, 0, 0)): # resize a rectangular image to a padded square + shape = img.shape[:2] # shape = [height, width] + ratio = float(height) / max(shape) + new_shape = [round(shape[0] * ratio), round(shape[1] * ratio)] + dw = height - new_shape[1] # width padding + dh = height - new_shape[0] # height padding + top, bottom = dh // 2, dh - (dh // 2) + left, right = dw // 2, dw - (dw // 2) + img = cv2.resize(img, (new_shape[1], new_shape[0]), interpolation=cv2.INTER_AREA) + return cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color), ratio, dw // 2, dh // 2 + + +def random_affine(img, targets=None, degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-3, 3), + borderValue=(0, 0, 0)): + # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) + # https://medium.com/uruvideo/dataset-augmentation-with-random-homographies-a8f4b44830d4 + + border = 0 # width of added border (optional) + height = max(img.shape[0], img.shape[1]) + border * 2 + + # Rotation and Scale + R = np.eye(3) + a = random.random() * (degrees[1] - degrees[0]) + degrees[0] + # a += random.choice([-180, -90, 0, 90]) # random 90deg rotations added to small rotations + + s = random.random() * (scale[1] - scale[0]) + scale[0] + R[:2] = cv2.getRotationMatrix2D(angle=a, center=(img.shape[1] / 2, img.shape[0] / 2), scale=s) + + # Translation + T = np.eye(3) + T[0, 2] = (random.random() * 2 - 1) * translate[0] * img.shape[0] + border # x translation (pixels) + T[1, 2] = (random.random() * 2 - 1) * translate[1] * img.shape[1] + border # y translation (pixels) + + # Shear + S = np.eye(3) + S[0, 1] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # x shear (deg) + S[1, 0] = math.tan((random.random() * (shear[1] - shear[0]) + shear[0]) * math.pi / 180) # y shear (deg) + + M = S @ T @ R # ORDER IS IMPORTANT HERE!! + imw = cv2.warpPerspective(img, M, dsize=(height, height), flags=cv2.INTER_LINEAR, + borderValue=borderValue) # BGR order (YUV-equalized BGR means) + + # Return warped points also + if targets is not None: + if len(targets) > 0: + n = targets.shape[0] + points = targets[:, 1:5].copy() + area0 = (points[:, 2] - points[:, 0]) * (points[:, 3] - points[:, 1]) + + # warp points + xy = np.ones((n * 4, 3)) + xy[:, :2] = points[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 + xy = (xy @ M.T)[:, :2].reshape(n, 8) + + # create new boxes + x = xy[:, [0, 2, 4, 6]] + y = xy[:, [1, 3, 5, 7]] + xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T + + # apply angle-based reduction + radians = a * math.pi / 180 + reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5 + x = (xy[:, 2] + xy[:, 0]) / 2 + y = (xy[:, 3] + xy[:, 1]) / 2 + w = (xy[:, 2] - xy[:, 0]) * reduction + h = (xy[:, 3] - xy[:, 1]) * reduction + xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T + + # reject warped points outside of image + np.clip(xy, 0, height, out=xy) + w = xy[:, 2] - xy[:, 0] + h = xy[:, 3] - xy[:, 1] + area = w * h + ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16)) + i = (w > 4) & (h > 4) & (area / area0 > 0.1) & (ar < 10) + + targets = targets[i] + targets[:, 1:5] = xy[i] + + return imw, targets, M + else: + return imw + + +def convert_tif2bmp(p='/Users/glennjocher/Downloads/DATA/xview/val_images_bmp'): + import glob + import cv2 + files = sorted(glob.glob('%s/*.tif' % p)) + for i, f in enumerate(files): + print('%g/%g' % (i + 1, len(files))) + + img = cv2.imread(f) + + cv2.imwrite(f.replace('.tif', '.bmp'), img) + os.system('rm -rf ' + f) diff --git a/utils/gcp.sh b/utils/gcp.sh new file mode 100644 index 00000000..765747ba --- /dev/null +++ b/utils/gcp.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash + +# Start +sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3 && cd yolov3 && python3 train.py -img_size 416 -epochs 999 + +# Resume +cd yolov3 && python3 train.py -img_size 416 -resume 1 + +# Detect +gsutil cp gs://ultralytics/fresh9_5_e201.pt yolov3/checkpoints +cd yolov3 && python3 detect.py + diff --git a/utils/parse_config.py b/utils/parse_config.py new file mode 100644 index 00000000..9dc03585 --- /dev/null +++ b/utils/parse_config.py @@ -0,0 +1,36 @@ + + +def parse_model_config(path): + """Parses the yolo-v3 layer configuration file and returns module definitions""" + file = open(path, 'r') + lines = file.read().split('\n') + lines = [x for x in lines if x and not x.startswith('#')] + lines = [x.rstrip().lstrip() for x in lines] # get rid of fringe whitespaces + module_defs = [] + for line in lines: + if line.startswith('['): # This marks the start of a new block + module_defs.append({}) + module_defs[-1]['type'] = line[1:-1].rstrip() + if module_defs[-1]['type'] == 'convolutional': + module_defs[-1]['batch_normalize'] = 0 + else: + key, value = line.split("=") + value = value.strip() + module_defs[-1][key.rstrip()] = value.strip() + + return module_defs + +def parse_data_config(path): + """Parses the data configuration file""" + options = dict() + options['gpus'] = '0,1,2,3' + options['num_workers'] = '10' + with open(path, 'r') as fp: + lines = fp.readlines() + for line in lines: + line = line.strip() + if line == '' or line.startswith('#'): + continue + key, value = line.split('=') + options[key.strip()] = value.strip() + return options diff --git a/utils/utils.py b/utils/utils.py new file mode 100755 index 00000000..67eaed19 --- /dev/null +++ b/utils/utils.py @@ -0,0 +1,372 @@ +import random + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F + +# set printoptions +torch.set_printoptions(linewidth=1320, precision=5, profile='long') +np.set_printoptions(linewidth=320, formatter={'float_kind': '{11.5g}'.format}) # format short g, %precision=5 + + +def load_classes(path): + """ + Loads class labels at 'path' + """ + fp = open(path, "r") + names = fp.read().split("\n")[:-1] + return names + + +def modelinfo(model): + nparams = sum(x.numel() for x in model.parameters()) + ngradients = sum(x.numel() for x in model.parameters() if x.requires_grad) + print('\n%4s %70s %9s %12s %20s %12s %12s' % ('', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma')) + for i, (name, p) in enumerate(model.named_parameters()): + name = name.replace('module_list.', '') + print('%4g %70s %9s %12g %20s %12g %12g' % ( + i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) + print('\n%g layers, %g parameters, %g gradients' % (i + 1, nparams, ngradients)) + + + +def xview_class_weights(indices): # weights of each class in the training set, normalized to mu = 1 + weights = 1 / torch.FloatTensor( + [74, 364, 713, 71, 2925, 209767, 6925, 1101, 3612, 12134, 5871, 3640, 860, 4062, 895, 149, 174, 17, 1624, 1846, + 125, 122, 124, 662, 1452, 697, 222, 190, 786, 200, 450, 295, 79, 205, 156, 181, 70, 64, 337, 1352, 336, 78, + 628, 841, 287, 83, 702, 1177, 313865, 195, 1081, 882, 1059, 4175, 123, 1700, 2317, 1579, 368, 85]) + weights /= weights.sum() + return weights[indices] + + + +def plot_one_box(x, im, color=None, label=None, line_thickness=None): + tl = line_thickness or round(0.003 * max(im.shape[0:2])) # line thickness + color = color or [random.randint(0, 255) for _ in range(3)] + c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) + cv2.rectangle(im, c1, c2, color, thickness=tl) + if label: + tf = max(tl - 1, 1) # font thickness + t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] + c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 + cv2.rectangle(im, c1, c2, color, -1) # filled + cv2.putText(im, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) + + +def weights_init_normal(m): + classname = m.__class__.__name__ + if classname.find('Conv') != -1: + torch.nn.init.normal_(m.weight.data, 0.0, 0.03) + elif classname.find('BatchNorm2d') != -1: + torch.nn.init.normal_(m.weight.data, 1.0, 0.03) + torch.nn.init.constant_(m.bias.data, 0.0) + + +def xyxy2xywh(box): + xywh = np.zeros(box.shape) + xywh[:, 0] = (box[:, 0] + box[:, 2]) / 2 + xywh[:, 1] = (box[:, 1] + box[:, 3]) / 2 + xywh[:, 2] = box[:, 2] - box[:, 0] + xywh[:, 3] = box[:, 3] - box[:, 1] + return xywh + + +def compute_ap(recall, precision): + """ Compute the average precision, given the recall and precision curves. + Code originally from https://github.com/rbgirshick/py-faster-rcnn. + # Arguments + recall: The recall curve (list). + precision: The precision curve (list). + # Returns + The average precision as computed in py-faster-rcnn. + """ + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], recall, [1.])) + mpre = np.concatenate(([0.], precision, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + + +def bbox_iou(box1, box2, x1y1x2y2=True): + # if len(box1.shape) == 1: + # box1 = box1.reshape(1, 4) + + """ + Returns the IoU of two bounding boxes + """ + if x1y1x2y2: + # Get the coordinates of bounding boxes + b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3] + b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3] + else: + # Transform from center and width to exact coordinates + b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2 + b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2 + b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2 + b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2 + + # get the corrdinates of the intersection rectangle + inter_rect_x1 = torch.max(b1_x1, b2_x1) + inter_rect_y1 = torch.max(b1_y1, b2_y1) + inter_rect_x2 = torch.min(b1_x2, b2_x2) + inter_rect_y2 = torch.min(b1_y2, b2_y2) + # Intersection area + inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1, 0) * torch.clamp(inter_rect_y2 - inter_rect_y1, 0) + # Union Area + b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1) + b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + + return inter_area / (b1_area + b2_area - inter_area + 1e-16) + + +def build_targets(pred_boxes, pred_conf, pred_cls, target, anchor_wh, nA, nC, nG, requestPrecision): + """ + returns nGT, nCorrect, tx, ty, tw, th, tconf, tcls + """ + nB = len(target) # target.shape[0] + nT = [len(x) for x in target] # torch.argmin(target[:, :, 4], 1) # targets per image + tx = torch.zeros(nB, nA, nG, nG) # batch size (4), number of anchors (3), number of grid points (13) + ty = torch.zeros(nB, nA, nG, nG) + tw = torch.zeros(nB, nA, nG, nG) + th = torch.zeros(nB, nA, nG, nG) + tconf = torch.ByteTensor(nB, nA, nG, nG).fill_(0) + tcls = torch.ByteTensor(nB, nA, nG, nG, nC).fill_(0) # nC = number of classes + TP = torch.ByteTensor(nB, max(nT)).fill_(0) + FP = torch.ByteTensor(nB, max(nT)).fill_(0) + FN = torch.ByteTensor(nB, max(nT)).fill_(0) + TC = torch.ShortTensor(nB, max(nT)).fill_(-1) # target category + + for b in range(nB): + nTb = nT[b] # number of targets + if nTb == 0: + continue + t = target[b] + FN[b, :nTb] = 1 + + # Convert to position relative to box + TC[b, :nTb], gx, gy, gw, gh = t[:, 0].long(), t[:, 1] * nG, t[:, 2] * nG, t[:, 3] * nG, t[:, 4] * nG + # Get grid box indices and prevent overflows (i.e. 13.01 on 13 anchors) + gi = torch.clamp(gx.long(), min=0, max=nG - 1) + gj = torch.clamp(gy.long(), min=0, max=nG - 1) + + # iou of targets-anchors (using wh only) + box1 = t[:, 3:5] * nG + # box2 = anchor_grid_wh[:, gj, gi] + box2 = anchor_wh.unsqueeze(1).repeat(1, nTb, 1) + inter_area = torch.min(box1, box2).prod(2) + iou_anch = inter_area / (gw * gh + box2.prod(2) - inter_area + 1e-16) + + # Select best iou_pred and anchor + iou_anch_best, a = iou_anch.max(0) # best anchor [0-2] for each target + + # Two targets can not claim the same anchor + if nTb > 1: + iou_order = np.argsort(-iou_anch_best) # best to worst + # u = torch.cat((gi, gj, a), 0).view(3, -1).numpy() + # _, first_unique = np.unique(u[:, iou_order], axis=1, return_index=True) # first unique indices + u = gi.float() * 0.4361538773074043 + gj.float() * 0.28012496588736746 + a.float() * 0.6627147212460307 + _, first_unique = np.unique(u[iou_order], return_index=True) # first unique indices + # print(((np.sort(first_unique) - np.sort(first_unique2)) ** 2).sum()) + i = iou_order[first_unique] + # best anchor must share significant commonality (iou) with target + i = i[iou_anch_best[i] > 0.10] + if len(i) == 0: + continue + + a, gj, gi, t = a[i], gj[i], gi[i], t[i] + if len(t.shape) == 1: + t = t.view(1, 5) + else: + if iou_anch_best < 0.10: + continue + i = 0 + + tc, gx, gy, gw, gh = t[:, 0].long(), t[:, 1] * nG, t[:, 2] * nG, t[:, 3] * nG, t[:, 4] * nG + + # Coordinates + tx[b, a, gj, gi] = gx - gi.float() + ty[b, a, gj, gi] = gy - gj.float() + # Width and height (sqrt method) + # tw[b, a, gj, gi] = torch.sqrt(gw / anchor_wh[a, 0]) / 2 + # th[b, a, gj, gi] = torch.sqrt(gh / anchor_wh[a, 1]) / 2 + # Width and height (yolov3 method) + tw[b, a, gj, gi] = torch.log(gw / anchor_wh[a, 0] + 1e-16) + th[b, a, gj, gi] = torch.log(gh / anchor_wh[a, 1] + 1e-16) + + # One-hot encoding of label + tcls[b, a, gj, gi, tc] = 1 + tconf[b, a, gj, gi] = 1 + + if requestPrecision: + # predicted classes and confidence + tb = torch.cat((gx - gw / 2, gy - gh / 2, gx + gw / 2, gy + gh / 2)).view(4, -1).t() # target boxes + pcls = torch.argmax(pred_cls[b, a, gj, gi], 1).cpu() + pconf = torch.sigmoid(pred_conf[b, a, gj, gi]).cpu() + iou_pred = bbox_iou(tb, pred_boxes[b, a, gj, gi].cpu()) + + TP[b, i] = (pconf > 0.99) & (iou_pred > 0.5) & (pcls == tc) + FP[b, i] = (pconf > 0.99) & (TP[b, i] == 0) # coordinates or class are wrong + FN[b, i] = pconf <= 0.99 # confidence score is too low (set to zero) + + return tx, ty, tw, th, tconf, tcls, TP, FP, FN, TC + + +def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4): + prediction = prediction.cpu() + + """ + Removes detections with lower object confidence score than 'conf_thres' and performs + Non-Maximum Suppression to further filter detections. + Returns detections with shape: + (x1, y1, x2, y2, object_conf, class_score, class_pred) + """ + + output = [None for _ in range(len(prediction))] + for image_i, pred in enumerate(prediction): + # Filter out confidence scores below threshold + # Get score and class with highest confidence + + # cross-class NMS + cross_class_nms = False + if cross_class_nms: + thresh = 0.85 + a = pred.clone() + a = a[np.argsort(-a[:, 4])] # sort best to worst + radius = 30 # area to search for cross-class ious + for i in range(len(a)): + if i >= len(a) - 1: + break + + close = (np.abs(a[i, 0] - a[i + 1:, 0]) < radius) & (np.abs(a[i, 1] - a[i + 1:, 1]) < radius) + close = close.nonzero() + + if len(close) > 0: + close = close + i + 1 + iou = bbox_iou(a[i:i + 1, :4], a[close.squeeze(), :4].reshape(-1, 4), x1y1x2y2=False) + bad = close[iou > thresh] + + if len(bad) > 0: + mask = torch.ones(len(a)).type(torch.ByteTensor) + mask[bad] = 0 + a = a[mask] + pred = a + + x, y, w, h = pred[:, 0].numpy(), pred[:, 1].numpy(), pred[:, 2].numpy(), pred[:, 3].numpy() + a = w * h # area + ar = w / (h + 1e-16) # aspect ratio + log_w, log_h, log_a, log_ar = np.log(w), np.log(h), np.log(a), np.log(ar) + + # n = len(w) + # shape_likelihood = np.zeros((n, 60), dtype=np.float32) + # x = np.concatenate((log_w.reshape(-1, 1), log_h.reshape(-1, 1)), 1) + # from scipy.stats import multivariate_normal + # for c in range(60): + # shape_likelihood[:, c] = multivariate_normal.pdf(x, mean=mat['class_mu'][c, :2], cov=mat['class_cov'][c, :2, :2]) + + class_prob, class_pred = torch.max(F.softmax(pred[:, 5:], 1), 1) + + v = ((pred[:, 4] > conf_thres) & (class_prob > .3)).numpy() + v = v.nonzero() + + pred = pred[v] + class_prob = class_prob[v] + class_pred = class_pred[v] + + # If none are remaining => process next image + nP = pred.shape[0] + if not nP: + continue + + # From (center x, center y, width, height) to (x1, y1, x2, y2) + box_corner = pred.new(nP, 4) + xy = pred[:, 0:2] + wh = pred[:, 2:4] / 2 + box_corner[:, 0:2] = xy - wh + box_corner[:, 2:4] = xy + wh + pred[:, :4] = box_corner + + # Detections ordered as (x1, y1, x2, y2, obj_conf, class_prob, class_pred) + detections = torch.cat((pred[:, :5], class_prob.float().unsqueeze(1), class_pred.float().unsqueeze(1)), 1) + # Iterate through all predicted classes + unique_labels = detections[:, -1].cpu().unique() + if prediction.is_cuda: + unique_labels = unique_labels.cuda() + + nms_style = 'OR' # 'AND' or 'OR' (classical) + for c in unique_labels: + # Get the detections with the particular class + detections_class = detections[detections[:, -1] == c] + # Sort the detections by maximum objectness confidence + _, conf_sort_index = torch.sort(detections_class[:, 4], descending=True) + detections_class = detections_class[conf_sort_index] + # Perform non-maximum suppression + max_detections = [] + + if nms_style == 'OR': # Classical NMS + while detections_class.shape[0]: + # Get detection with highest confidence and save as max detection + max_detections.append(detections_class[0].unsqueeze(0)) + # Stop if we're at the last detection + if len(detections_class) == 1: + break + # Get the IOUs for all boxes with lower confidence + ious = bbox_iou(max_detections[-1], detections_class[1:]) + + # Remove detections with IoU >= NMS threshold + detections_class = detections_class[1:][ious < nms_thres] + + elif nms_style == 'AND': # 'AND'-style NMS, at least two boxes must share commonality to pass, single boxes erased + while detections_class.shape[0]: + if len(detections_class) == 1: + break + + ious = bbox_iou(detections_class[:1], detections_class[1:]) + + if ious.max() > 0.5: + max_detections.append(detections_class[0].unsqueeze(0)) + + # Remove detections with IoU >= NMS threshold + detections_class = detections_class[1:][ious < nms_thres] + + if len(max_detections) > 0: + max_detections = torch.cat(max_detections).data + # Add max detections to outputs + output[image_i] = max_detections if output[image_i] is None else torch.cat( + (output[image_i], max_detections)) + + return output + + +def strip_optimizer_from_checkpoint(filename='checkpoints/best.pt'): + # Strip optimizer from *.pt files for lighter files (reduced by 2/3 size) + import torch + a = torch.load(filename, map_location='cpu') + a['optimizer'] = [] + torch.save(a, filename.replace('.pt', '_lite.pt')) + + +def plotResults(): + # Plot YOLO training results file "results.txt" + import numpy as np + import matplotlib.pyplot as plt + plt.figure(figsize=(18, 9)) + s = ['x', 'y', 'w', 'h', 'conf', 'cls', 'loss', 'prec', 'recall'] + for f in ('results.txt',): + results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 9, 10]).T + for i in range(9): + plt.subplot(2, 5, i + 1) + plt.plot(results[i, :3000], marker='.', label=f) + plt.title(s[i]) + plt.legend()