Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2020-01-17 19:42:04 -08:00
parent bab855507a
commit 3bac3c63b1
3 changed files with 13 additions and 11 deletions
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2
test.py
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@ -213,7 +213,7 @@ if __name__ == '__main__':
parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch') parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)') parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold') parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.5, help='IOU threshold for NMS') parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS')
parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file') parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
parser.add_argument('--task', default='test', help="'test', 'study', 'benchmark'") parser.add_argument('--task', default='test', help="'test', 'study', 'benchmark'")
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu') parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')

16
train.py
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@ -52,7 +52,7 @@ if f:
def train(): def train():
cfg = opt.cfg cfg = opt.cfg
data = opt.data data = opt.data
img_size = opt.img_size img_size, img_size_test = opt.img_size if len(opt.img_size) == 2 else opt.img_size * 2 # train, test sizes
epochs = opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs epochs = opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs
batch_size = opt.batch_size batch_size = opt.batch_size
accumulate = opt.accumulate # effective bs = batch_size * accumulate = 16 * 4 = 64 accumulate = opt.accumulate # effective bs = batch_size * accumulate = 16 * 4 = 64
@ -191,7 +191,7 @@ def train():
collate_fn=dataset.collate_fn) collate_fn=dataset.collate_fn)
# Testloader # Testloader
testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path, opt.img_size, batch_size * 2, testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path, img_size_test, batch_size * 2,
hyp=hyp, hyp=hyp,
rect=True, rect=True,
cache_labels=True, cache_labels=True,
@ -221,7 +221,7 @@ def train():
# Prebias # Prebias
if prebias: if prebias:
if epoch < 1: # prebias if epoch < 3: # prebias
ps = 0.1, 0.9 # prebias settings (lr=0.1, momentum=0.9) ps = 0.1, 0.9 # prebias settings (lr=0.1, momentum=0.9)
else: # normal training else: # normal training
ps = hyp['lr0'], hyp['momentum'] # normal training settings ps = hyp['lr0'], hyp['momentum'] # normal training settings
@ -314,10 +314,10 @@ def train():
results, maps = test.test(cfg, results, maps = test.test(cfg,
data, data,
batch_size=batch_size * 2, batch_size=batch_size * 2,
img_size=opt.img_size, img_size=img_size_test,
model=model, model=model,
conf_thres=0.001 if final_epoch else 0.1, # 0.1 for speed conf_thres=0.001 if final_epoch and is_coco else 0.1, # 0.1 for speed
iou_thres=0.6 if final_epoch and is_coco else 0.5, iou_thres=0.6,
save_json=final_epoch and is_coco, save_json=final_epoch and is_coco,
single_cls=opt.single_cls, single_cls=opt.single_cls,
dataloader=testloader) dataloader=testloader)
@ -402,7 +402,7 @@ if __name__ == '__main__':
parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path') parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path')
parser.add_argument('--data', type=str, default='data/coco2017.data', help='*.data path') parser.add_argument('--data', type=str, default='data/coco2017.data', help='*.data path')
parser.add_argument('--multi-scale', action='store_true', help='adjust (67% - 150%) img_size every 10 batches') parser.add_argument('--multi-scale', action='store_true', help='adjust (67% - 150%) img_size every 10 batches')
parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)') parser.add_argument('--img-size', nargs='+', type=int, default=[416], help='train and test image-sizes')
parser.add_argument('--rect', action='store_true', help='rectangular training') parser.add_argument('--rect', action='store_true', help='rectangular training')
parser.add_argument('--resume', action='store_true', help='resume training from last.pt') parser.add_argument('--resume', action='store_true', help='resume training from last.pt')
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
@ -425,7 +425,7 @@ if __name__ == '__main__':
mixed_precision = False mixed_precision = False
# scale hyp['obj'] by img_size (evolved at 320) # scale hyp['obj'] by img_size (evolved at 320)
# hyp['obj'] *= opt.img_size / 320. # hyp['obj'] *= opt.img_size[0] / 320.
tb_writer = None tb_writer = None
if not opt.evolve: # Train normally if not opt.evolve: # Train normally

6
utils/gcp.sh
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@ -308,8 +308,10 @@ n=209 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gp
n=210 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 1000 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --bucket ult/wer --name $n --nosave --cache --device 1 --cfg yolov3-tiny-3cls.cfg n=210 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 1000 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --bucket ult/wer --name $n --nosave --cache --device 1 --cfg yolov3-tiny-3cls.cfg
n=216 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 1000 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --bucket ult/wer --name $n --nosave --cache --device 0 --cfg yolov3-tiny-3cls.cfg n=216 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 1000 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --bucket ult/wer --name $n --nosave --cache --device 0 --cfg yolov3-tiny-3cls.cfg
n=218 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all --ipc=host -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 1000 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc default --pre --multi --bucket ult/wer --name $n --nosave --cache --device 7 --cfg yolov3-tiny-3cls.cfg n=218 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all --ipc=host -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 1000 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc default --pre --multi --bucket ult/wer --name $n --nosave --cache --device 7 --cfg yolov3-tiny-3cls.cfg
n=230 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all --ipc=host -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 100 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc default --multi --bucket ult/wer --name $n --nosave --cache --device 0 --cfg yolov3-tiny-1cls.cfg --single n=230 && t=ultralytics/athena:v$n && sudo docker pull $t && sudo docker run --gpus all --ipc=host -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 100 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --multi --bucket ult/wer --name $n --nosave --cache --device 0 --cfg yolov3-tiny-1cls.cfg --single
n=231 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all --ipc=host -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 100 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc default --multi --bucket ult/wer --name $n --nosave --cache --device 1 --cfg yolov3-tiny-1cls.cfg --single n=231 && t=ultralytics/athena:v$n && sudo docker pull $t && sudo docker run --gpus all --ipc=host -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 100 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --multi --bucket ult/wer --name $n --nosave --cache --device 1 --cfg yolov3-tiny-1cls.cfg --single
n=232 && t=ultralytics/athena:v$n && sudo docker pull $t && sudo docker run --gpus all --ipc=host -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 100 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --multi --bucket ult/wer --name $n --nosave --cache --device 0 --cfg yolov3-tiny-1cls.cfg --single
n=233 && t=ultralytics/athena:v$n && sudo docker pull $t && sudo docker run --gpus all --ipc=host -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 100 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --multi --bucket ult/wer --name $n --nosave --cache --device 0 --cfg yolov3-tiny-1cls.cfg --single
n=206 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -it --ipc=host -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 10 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --nosave --cache --device 0 --cfg yolov3-tiny-3cls.cfg n=206 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -it --ipc=host -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 10 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --nosave --cache --device 0 --cfg yolov3-tiny-3cls.cfg