130 lines
7.2 KiB
Bash
Executable File
130 lines
7.2 KiB
Bash
Executable File
#!/usr/bin/env bash
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# New VM
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rm -rf sample_data yolov3 darknet apex coco cocoapi knife knifec
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git clone https://github.com/ultralytics/yolov3
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# git clone https://github.com/AlexeyAB/darknet && cd darknet && make GPU=1 CUDNN=1 CUDNN_HALF=1 OPENCV=0 && wget -c https://pjreddie.com/media/files/darknet53.conv.74 && cd ..
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git clone https://github.com/NVIDIA/apex && cd apex && pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" . --user && cd .. && rm -rf apex
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# git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3
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sudo conda install -y -c conda-forge scikit-image tensorboard pycocotools
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python3 -c "
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from yolov3.utils.google_utils import gdrive_download
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gdrive_download('1HaXkef9z6y5l4vUnCYgdmEAj61c6bfWO','coco.zip')"
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sudo shutdown
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# Re-clone
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rm -rf yolov3 # Warning: remove existing
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git clone https://github.com/ultralytics/yolov3 && cd yolov3 # master
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# git clone -b test --depth 1 https://github.com/ultralytics/yolov3 test # branch
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python3 train.py --img-size 320 --weights weights/darknet53.conv.74 --epochs 27 --batch-size 64 --accumulate 1
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# Train
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python3 train.py
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# Resume
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python3 train.py --resume
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# Detect
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python3 detect.py
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# Test
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python3 test.py --save-json
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# Evolve
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for i in {0..500}
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do
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python3 train.py --data data/coco.data --img-size 320 --epochs 1 --batch-size 64 --accumulate 1 --evolve --bucket yolov4
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done
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# Git pull
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git pull https://github.com/ultralytics/yolov3 # master
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git pull https://github.com/ultralytics/yolov3 test # branch
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# Test Darknet training
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python3 test.py --weights ../darknet/backup/yolov3.backup
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# Copy last.pt TO bucket
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gsutil cp yolov3/weights/last1gpu.pt gs://ultralytics
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# Copy last.pt FROM bucket
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gsutil cp gs://ultralytics/last.pt yolov3/weights/last.pt
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wget https://storage.googleapis.com/ultralytics/yolov3/last_v1_0.pt -O weights/last_v1_0.pt
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wget https://storage.googleapis.com/ultralytics/yolov3/best_v1_0.pt -O weights/best_v1_0.pt
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# Reproduce tutorials
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rm results*.txt # WARNING: removes existing results
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python3 train.py --nosave --data data/coco_1img.data && mv results.txt results0r_1img.txt
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python3 train.py --nosave --data data/coco_10img.data && mv results.txt results0r_10img.txt
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python3 train.py --nosave --data data/coco_100img.data && mv results.txt results0r_100img.txt
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# python3 train.py --nosave --data data/coco_100img.data --transfer && mv results.txt results3_100imgTL.txt
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python3 -c "from utils import utils; utils.plot_results()"
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# gsutil cp results*.txt gs://ultralytics
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gsutil cp results.png gs://ultralytics
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sudo shutdown
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# Reproduce mAP
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python3 test.py --save-json --img-size 608
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python3 test.py --save-json --img-size 416
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python3 test.py --save-json --img-size 320
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sudo shutdown
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# Benchmark script
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git clone https://github.com/ultralytics/yolov3 # clone our repo
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git clone https://github.com/NVIDIA/apex && cd apex && pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" . --user && cd .. && rm -rf apex # install nvidia apex
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python3 -c "from yolov3.utils.google_utils import gdrive_download; gdrive_download('1HaXkef9z6y5l4vUnCYgdmEAj61c6bfWO','coco.zip')" # download coco dataset (20GB)
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cd yolov3 && clear && python3 train.py --epochs 1 # run benchmark (~30 min)
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# Unit tests
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python3 detect.py # detect 2 persons, 1 tie
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python3 test.py --data data/coco_32img.data # test mAP = 0.8
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python3 train.py --data data/coco_32img.data --epochs 5 --nosave # train 5 epochs
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python3 train.py --data data/coco_1cls.data --epochs 5 --nosave # train 5 epochs
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python3 train.py --data data/coco_1img.data --epochs 5 --nosave # train 5 epochs
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# AlexyAB Darknet
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gsutil cp -r gs://sm6/supermarket2 . # dataset from bucket
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rm -rf darknet && git clone https://github.com/AlexeyAB/darknet && cd darknet && wget -c https://pjreddie.com/media/files/darknet53.conv.74 # sudo apt install libopencv-dev && make
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./darknet detector calc_anchors data/coco_img64.data -num_of_clusters 9 -width 320 -height 320 # kmeans anchor calculation
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./darknet detector train ../supermarket2/supermarket2.data ../yolo_v3_spp_pan_scale.cfg darknet53.conv.74 -map -dont_show # train spp
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./darknet detector train ../yolov3/data/coco.data ../yolov3-spp.cfg darknet53.conv.74 -map -dont_show # train spp coco
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./darknet detector train data/coco.data ../yolov3-spp.cfg darknet53.conv.74 -map -dont_show # train spp
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gsutil cp -r backup/*5000.weights gs://sm6/weights
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sudo shutdown
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./darknet detector train ../supermarket2/supermarket2.data ../yolov3-tiny-sm2-1cls.cfg yolov3-tiny.conv.15 -map -dont_show # train tiny
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./darknet detector train ../supermarket2/supermarket2.data cfg/yolov3-spp-sm2-1cls.cfg backup/yolov3-spp-sm2-1cls_last.weights # resume
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python3 train.py --data ../supermarket2/supermarket2.data --cfg ../yolov3-spp-sm2-1cls.cfg --epochs 100 --num-workers 8 --img-size 320 --nosave # train ultralytics
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python3 test.py --data ../supermarket2/supermarket2.data --weights ../darknet/backup/yolov3-spp-sm2-1cls_5000.weights --cfg cfg/yolov3-spp-sm2-1cls.cfg # test
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gsutil cp -r backup/*.weights gs://sm6/weights # weights to bucket
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python3 test.py --data ../supermarket2/supermarket2.data --weights weights/yolov3-spp-sm2-1cls_5000.weights --cfg ../yolov3-spp-sm2-1cls.cfg --img-size 320 --conf-thres 0.2 # test
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python3 test.py --data ../supermarket2/supermarket2.data --weights weights/yolov3-spp-sm2-1cls-scalexy_125_5000.weights --cfg ../yolov3-spp-sm2-1cls-scalexy_125.cfg --img-size 320 --conf-thres 0.2 # test
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python3 test.py --data ../supermarket2/supermarket2.data --weights weights/yolov3-spp-sm2-1cls-scalexy_150_5000.weights --cfg ../yolov3-spp-sm2-1cls-scalexy_150.cfg --img-size 320 --conf-thres 0.2 # test
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python3 test.py --data ../supermarket2/supermarket2.data --weights weights/yolov3-spp-sm2-1cls-scalexy_200_5000.weights --cfg ../yolov3-spp-sm2-1cls-scalexy_200.cfg --img-size 320 --conf-thres 0.2 # test
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python3 test.py --data ../supermarket2/supermarket2.data --weights ../darknet/backup/yolov3-spp-sm2-1cls-scalexy_variable_5000.weights --cfg ../yolov3-spp-sm2-1cls-scalexy_variable.cfg --img-size 320 --conf-thres 0.2 # test
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python3 train.py --img-size 320 --epochs 27 --batch-size 64 --accumulate 1 --nosave --notest && python3 test.py --weights weights/last.pt --img-size 320 --save-json && sudo shutdown
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# Debug/Development
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python3 train.py --data data/coco.data --img-size 320 --single-scale --batch-size 64 --accumulate 1 --epochs 1 --evolve --giou
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python3 test.py --weights weights/last.pt --cfg cfg/yolov3-spp.cfg --img-size 320
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gsutil cp evolve.txt gs://ultralytics
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sudo shutdown
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#Docker
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sudo docker kill $(sudo docker ps -q)
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sudo docker pull ultralytics/yolov3:v1
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sudo nvidia-docker run -it --ipc=host --mount type=bind,source="$(pwd)"/coco,target=/usr/src/coco ultralytics/yolov3:v1
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clear
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while true
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do
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python3 train.py --data data/coco.data --img-size 320 --batch-size 64 --accumulate 1 --evolve --epochs 1 --adam --bucket yolov4/adamdefaultpw_coco_1e --device 1
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done
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python3 train.py --data data/coco.data --img-size 320 --batch-size 64 --accumulate 1 --epochs 1 --adam --device 1 --prebias
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while true; do python3 train.py --data data/coco.data --img-size 320 --batch-size 64 --accumulate 1 --evolve --epochs 1 --adam --bucket yolov4/adamdefaultpw_coco_1e; done
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