updates
This commit is contained in:
Glenn Jocher
parent
cdb4680390
commit
dec2c7d9a6
9
test.py
9
test.py
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@ -17,7 +17,8 @@ def test(cfg,
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iou_thres=0.5, # for nms
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save_json=False,
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model=None,
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dataloader=None):
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dataloader=None,
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single_cls=False):
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# Initialize/load model and set device
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if model is None:
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device = torch_utils.select_device(opt.device, batch_size=batch_size)
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@ -45,7 +46,7 @@ def test(cfg,
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# Configure run
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data = parse_data_cfg(data)
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nc = int(data['classes']) # number of classes
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nc = 1 if single_cls else int(data['classes']) # number of classes
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path = data['valid'] # path to test images
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names = load_classes(data['names']) # class names
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iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
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@ -216,6 +217,7 @@ if __name__ == '__main__':
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parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
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parser.add_argument('--task', default='test', help="'test', 'study', 'benchmark'")
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parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
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parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
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opt = parser.parse_args()
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opt.save_json = opt.save_json or any([x in opt.data for x in ['coco.data', 'coco2014.data', 'coco2017.data']])
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print(opt)
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@ -229,7 +231,8 @@ if __name__ == '__main__':
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opt.img_size,
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opt.conf_thres,
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opt.iou_thres,
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opt.save_json)
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opt.save_json,
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opt.single_cls)
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elif opt.task == 'benchmark':
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# mAPs at 320-608 at conf 0.5 and 0.7
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44
train.py
44
train.py
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@ -74,7 +74,7 @@ def train():
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data_dict = parse_data_cfg(data)
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train_path = data_dict['train']
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test_path = data_dict['valid']
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nc = int(data_dict['classes']) # number of classes
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nc = 1 if opt.single_cls else int(data_dict['classes']) # number of classes
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# Remove previous results
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for f in glob.glob('*_batch*.jpg') + glob.glob(results_file):
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@ -177,7 +177,8 @@ def train():
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hyp=hyp, # augmentation hyperparameters
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rect=opt.rect, # rectangular training
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cache_labels=True,
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cache_images=opt.cache_images)
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cache_images=opt.cache_images,
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single_cls=opt.single_cls)
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# Dataloader
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batch_size = min(batch_size, len(dataset))
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@ -194,7 +195,8 @@ def train():
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hyp=hyp,
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rect=True,
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cache_labels=True,
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cache_images=opt.cache_images),
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cache_images=opt.cache_images,
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single_cls=opt.single_cls),
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batch_size=batch_size * 2,
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num_workers=nw,
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pin_memory=True,
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@ -202,6 +204,7 @@ def train():
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# Start training
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nb = len(dataloader)
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prebias = start_epoch == 0
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model.nc = nc # attach number of classes to model
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model.arc = opt.arc # attach yolo architecture
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model.hyp = hyp # attach hyperparameters to model
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@ -213,24 +216,22 @@ def train():
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torch_utils.model_info(model, report='summary') # 'full' or 'summary'
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print('Using %g dataloader workers' % nw)
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print('Starting training for %g epochs...' % epochs)
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for epoch in range(start_epoch - 1 if opt.prebias else start_epoch, epochs): # epoch ------------------------------
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for epoch in range(start_epoch, epochs): # epoch ------------------------------
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model.train()
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print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
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# Prebias
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if opt.prebias:
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if epoch < 0: # prebias
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ps = 0.1, 0.9, False # prebias settings (lr=0.1, momentum=0.9, requires_grad=False)
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if prebias:
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if epoch < 20: # prebias
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ps = 0.1, 0.9 # prebias settings (lr=0.1, momentum=0.9)
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else: # normal training
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ps = hyp['lr0'], hyp['momentum'], True # normal training settings
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opt.prebias = False
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ps = hyp['lr0'], hyp['momentum'] # normal training settings
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print_model_biases(model)
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prebias = False
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for p in optimizer.param_groups:
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p['lr'] = ps[0] # learning rate
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if p.get('momentum') is not None: # for SGD but not Adam
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p['momentum'] = ps[1]
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for name, p in model.named_parameters():
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p.requires_grad = True if name.endswith('.bias') else ps[2]
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# Bias optimizer settings
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optimizer.param_groups[2]['lr'] = ps[0]
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if optimizer.param_groups[2].get('momentum') is not None: # for SGD but not Adam
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optimizer.param_groups[2]['momentum'] = ps[1]
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# Update image weights (optional)
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if dataset.image_weights:
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@ -239,6 +240,7 @@ def train():
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dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # rand weighted idx
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mloss = torch.zeros(4).to(device) # mean losses
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print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
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pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
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for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
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ni = i + nb * epoch # number integrated batches (since train start)
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@ -307,10 +309,7 @@ def train():
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# Process epoch results
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final_epoch = epoch + 1 == epochs
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if opt.prebias:
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print_model_biases(model)
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continue
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elif not opt.notest or final_epoch: # Calculate mAP
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if not opt.notest or final_epoch: # Calculate mAP
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is_coco = any([x in data for x in ['coco.data', 'coco2014.data', 'coco2017.data']]) and model.nc == 80
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results, maps = test.test(cfg,
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data,
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@ -320,7 +319,8 @@ def train():
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conf_thres=0.001 if final_epoch else 0.1, # 0.1 for speed
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iou_thres=0.6 if final_epoch and is_coco else 0.5,
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save_json=final_epoch and is_coco,
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dataloader=testloader)
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dataloader=testloader,
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single_cls=opt.single_cls)
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# Update scheduler
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scheduler.step()
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@ -412,10 +412,10 @@ if __name__ == '__main__':
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parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
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parser.add_argument('--weights', type=str, default='weights/ultralytics68.pt', help='initial weights')
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parser.add_argument('--arc', type=str, default='default', help='yolo architecture') # defaultpw, uCE, uBCE
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parser.add_argument('--prebias', action='store_true', help='pretrain model biases')
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parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
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parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)')
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parser.add_argument('--adam', action='store_true', help='use adam optimizer')
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parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
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parser.add_argument('--var', type=float, help='debug variable')
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opt = parser.parse_args()
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opt.weights = last if opt.resume else opt.weights
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@ -263,7 +263,7 @@ class LoadStreams: # multiple IP or RTSP cameras
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class LoadImagesAndLabels(Dataset): # for training/testing
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def __init__(self, path, img_size=416, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
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cache_labels=False, cache_images=False):
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cache_labels=False, cache_images=False, single_cls=False):
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path = str(Path(path)) # os-agnostic
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assert os.path.isfile(path), 'File not found %s. See %s' % (path, help_url)
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with open(path, 'r') as f:
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@ -343,7 +343,8 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels: %s' % file
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if np.unique(l, axis=0).shape[0] < l.shape[0]: # duplicate rows
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nd += 1 # print('WARNING: duplicate rows in %s' % self.label_files[i]) # duplicate rows
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if single_cls:
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l[:, 0] = 0 # force dataset into single-class mode
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self.labels[i] = l
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nf += 1 # file found
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@ -8,8 +8,8 @@
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#t=ultralytics/yolov3:v199 && sudo docker pull $t && sudo nvidia-docker run -it --ipc=host -v "$(pwd)"/coco:/usr/src/coco $t python3 train.py --data coco2014.data --img-size 672 --epochs 10 --batch 16 --accum 4 --weights '' --arc defaultpw --device 0 --multi
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while true; do
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python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 100 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --bucket ult/wer --evolve --device $1 --cfg yolov3-tiny-3cls.cfg --cache
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# python3 train.py --data ../out/data.data --img-size 608 --epochs 10 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --bucket ult/athena --evolve --device $1 --cfg yolov3-spp-1cls.cfg
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# python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 100 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc default --pre --multi --bucket ult/wer --evolve --device $1 --cfg yolov3-tiny-3cls.cfg --cache
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python3 train.py --data ../out/data.data --img-size 608 --epochs 10 --batch 8 --accum 8 --weights ultralytics68.pt --arc default --pre --multi --bucket ult/athena --evolve --device $1 --cfg yolov3-spp-1cls.cfg
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# python3 train.py --data coco2014.data --img-size 640 --epochs 10 --batch 22 --accum 3 --evolve --weights '' --arc defaultpw --pre --bucket yolov4/640ms_coco2014_10e --device $1 --multi
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# python3 train.py --data coco2014.data --img-size 320 --epochs 27 --batch 64 --accum 1 --evolve --weights '' --arc defaultpw --pre --bucket yolov4/320_coco2014_27e --device $1
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34
utils/gcp.sh
34
utils/gcp.sh
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@ -38,11 +38,17 @@ python3 detect.py
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# Test
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python3 test.py --save-json
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# Kill All
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t=ultralytics/yolov3:v206
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docker kill $(docker ps -a -q --filter ancestor=$t)
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t=ultralytics/yolov3:v208
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docker kill $(docker ps -a -q --filter ancestor=$t)
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# Evolve
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sudo -s
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t=ultralytics/yolov3:v206
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docker kill $(docker ps -a -q --filter ancestor=$t)
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for i in 6 7
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for i in 4 5 6 7
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do
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docker pull $t && docker run --gpus all -d --ipc=host -v "$(pwd)"/data:/usr/src/data $t bash utils/evolve.sh $i
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# docker pull $t && docker run --gpus all -d --ipc=host -v "$(pwd)"/out:/usr/src/out $t bash utils/evolve.sh $i
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@ -55,10 +61,10 @@ done
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sudo -s
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t=ultralytics/yolov3:v208
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docker kill $(docker ps -a -q --filter ancestor=$t)
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for i in 0
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for i in 0 1
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do
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# docker pull $t && docker run --gpus all -d --ipc=host -v "$(pwd)"/data:/usr/src/data $t bash utils/evolve.sh $i
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docker pull $t && docker run --gpus all -it --ipc=host -v "$(pwd)"/out:/usr/src/out $t bash utils/evolve.sh $i
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docker pull $t && docker run --gpus all -d --ipc=host -v "$(pwd)"/out:/usr/src/out $t bash utils/evolve.sh $i
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# docker pull $t && nvidia-docker run -d -v "$(pwd)"/coco:/usr/src/coco $t bash utils/evolve.sh $i
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# docker pull $t && nvidia-docker run -d -v /mnt/disks/nvme0n1/coco:/usr/src/coco $t bash utils/evolve.sh $i
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sleep 180
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@ -277,6 +283,19 @@ n=211 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gp
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n=212 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 100 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 0 --bucket ult/athena --name $n --cfg yolov3-spp-1cls.cfg
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n=213 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 100 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 0 --bucket ult/athena --name $n --cfg yolov3-spp-1cls.cfg
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n=214 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 100 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 0 --bucket ult/athena --name $n --cfg yolov3-spp-1cls.cfg
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n=215 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 100 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 0 --bucket ult/athena --name $n --cfg yolov3-spp-1cls.cfg
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n=217 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all --ipc=host -it -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 100 --batch 8 --accum 8 --weights ultralytics68.pt --arc default --pre --multi --device 6 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
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n=219 && t=ultralytics/yolov3:v215 && sudo docker pull $t && sudo docker run -d --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 10 --batch 8 --accum 8 --weights ultralytics68.pt --arc default --pre --multi --device 0 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
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n=220 && t=ultralytics/yolov3:v215 && sudo docker pull $t && sudo docker run -d --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 20 --batch 8 --accum 8 --weights ultralytics68.pt --arc default --pre --multi --device 1 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
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n=221 && t=ultralytics/yolov3:v215 && sudo docker pull $t && sudo docker run -d --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 30 --batch 8 --accum 8 --weights ultralytics68.pt --arc default --pre --multi --device 2 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
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n=222 && t=ultralytics/yolov3:v215 && sudo docker pull $t && sudo docker run -d --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 40 --batch 8 --accum 8 --weights ultralytics68.pt --arc default --pre --multi --device 3 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
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n=223 && t=ultralytics/yolov3:v215 && sudo docker pull $t && sudo docker run -d --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 10 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 0 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
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n=224 && t=ultralytics/yolov3:v215 && sudo docker pull $t && sudo docker run -d --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 20 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 1 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
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n=225 && t=ultralytics/yolov3:v215 && sudo docker pull $t && sudo docker run -d --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 30 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 0 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
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n=226 && t=ultralytics/yolov3:v215 && sudo docker pull $t && sudo docker run -d --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 40 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --pre --multi --device 0 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
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n=227 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 10 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --multi --device 0 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
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n=228 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 20 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --multi --device 0 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
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n=229 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/out:/usr/src/out $t python3 train.py --data ../out/data.data --img-size 608 --epochs 20 --batch 8 --accum 8 --weights ultralytics68.pt --arc defaultpw --multi --device 0 --bucket ult/athena --name $n --nosave --cfg yolov3-spp-1cls.cfg
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# sm4
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n=201 && t=ultralytics/yolov3:v201 && sudo docker pull $t && sudo nvidia-docker run -d -v "$(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 --device 0 --cfg yolov3-tiny-3cls.cfg
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@ -287,3 +306,12 @@ n=205 && t=ultralytics/yolov3:v202 && sudo docker pull $t && sudo nvidia-docker
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n=206 && t=ultralytics/yolov3:v$n && sudo docker pull $t && sudo docker run --gpus all -it -v "$(pwd)"/data:/usr/src/data $t python3 train.py --data ../data/sm4/out.data --img-size 320 --epochs 100 --batch 64 --accum 1 --weights yolov3-tiny.pt --arc defaultpw --pre --multi --notest --nosave --cache --device 0 --cfg yolov3-tiny-3cls.cfg
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n=209 && t=ultralytics/yolov3:v$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 $n --nosave --cache --device 3 --cfg yolov3-tiny-3cls.cfg
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n=210 && t=ultralytics/yolov3:v$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 $n --nosave --cache --device 0 --cfg yolov3-tiny-3cls.cfg
|
||||
n=218 && t=ultralytics/yolov3:v$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 $n --nosave --cache --device 0 --cfg yolov3-tiny-1cls.cfg --single
|
||||
n=231 && t=ultralytics/yolov3:v$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 $n --nosave --cache --device 1 --cfg yolov3-tiny-1cls.cfg --single
|
||||
|
||||
|
||||
n=206 && t=ultralytics/yolov3:v$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 1 --cfg yolov3-tiny-3cls.cfg
|
||||
|
||||
|
|
|
|||
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Reference in New Issue