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modify readme for yolov3_darknet53

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pull/5135/head
yangyongjie 5 years ago
parent 5c9348ab17
commit b9a2e771c6
2 changed files with 286 additions and 82 deletions
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model_zoo/official/cv/yolov3_darknet53/README.md
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model_zoo/official/cv/yolov3_darknet53/train.py
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@ -63,54 +63,58 @@ def parse_args():
parser = argparse.ArgumentParser('mindspore coco training')
# dataset related
parser.add_argument('--data_dir', type=str, default='', help='train data dir')
parser.add_argument('--per_batch_size', default=32, type=int, help='batch size for per gpu')
parser.add_argument('--data_dir', type=str, help='Train dataset directory.')
parser.add_argument('--per_batch_size', default=32, type=int, help='Batch size for Training. Default: 32.')
# network related
parser.add_argument('--pretrained_backbone', default='', type=str, help='model_path, local pretrained backbone'
' model to load')
parser.add_argument('--resume_yolov3', default='', type=str, help='path of pretrained yolov3')
parser.add_argument('--pretrained_backbone', default='', type=str,
help='The ckpt file of DarkNet53. Default: "".')
parser.add_argument('--resume_yolov3', default='', type=str,
help='The ckpt file of YOLOv3, which used to fine tune. Default: ""')
# optimizer and lr related
parser.add_argument('--lr_scheduler', default='exponential', type=str,
help='lr-scheduler, option type: exponential, cosine_annealing')
parser.add_argument('--lr', default=0.001, type=float, help='learning rate of the training')
parser.add_argument('--lr_epochs', type=str, default='220,250', help='epoch of lr changing')
help='Learning rate scheduler, options: exponential, cosine_annealing. Default: exponential')
parser.add_argument('--lr', default=0.001, type=float, help='Learning rate. Default: 0.001')
parser.add_argument('--lr_epochs', type=str, default='220,250',
help='Epoch of changing of lr changing, split with ",". Default: 220,250')
parser.add_argument('--lr_gamma', type=float, default=0.1,
help='decrease lr by a factor of exponential lr_scheduler')
parser.add_argument('--eta_min', type=float, default=0., help='eta_min in cosine_annealing scheduler')
parser.add_argument('--T_max', type=int, default=320, help='T-max in cosine_annealing scheduler')
parser.add_argument('--max_epoch', type=int, default=320, help='max epoch num to train the model')
parser.add_argument('--warmup_epochs', default=0, type=float, help='warmup epoch')
parser.add_argument('--weight_decay', type=float, default=0.0005, help='weight decay')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
help='Decrease lr by a factor of exponential lr_scheduler. Default: 0.1')
parser.add_argument('--eta_min', type=float, default=0., help='Eta_min in cosine_annealing scheduler. Default: 0')
parser.add_argument('--T_max', type=int, default=320, help='T-max in cosine_annealing scheduler. Default: 320')
parser.add_argument('--max_epoch', type=int, default=320, help='Max epoch num to train the model. Default: 320')
parser.add_argument('--warmup_epochs', default=0, type=float, help='Warmup epochs. Default: 0')
parser.add_argument('--weight_decay', type=float, default=0.0005, help='Weight decay factor. Default: 0.0005')
parser.add_argument('--momentum', type=float, default=0.9, help='Momentum. Default: 0.9')
# loss related
parser.add_argument('--loss_scale', type=int, default=1024, help='static loss scale')
parser.add_argument('--label_smooth', type=int, default=0, help='whether to use label smooth in CE')
parser.add_argument('--label_smooth_factor', type=float, default=0.1, help='smooth strength of original one-hot')
parser.add_argument('--loss_scale', type=int, default=1024, help='Static loss scale. Default: 1024')
parser.add_argument('--label_smooth', type=int, default=0, help='Whether to use label smooth in CE. Default:0')
parser.add_argument('--label_smooth_factor', type=float, default=0.1,
help='Smooth strength of original one-hot. Default: 0.1')
# logging related
parser.add_argument('--log_interval', type=int, default=100, help='logging interval')
parser.add_argument('--ckpt_path', type=str, default='outputs/', help='checkpoint save location')
parser.add_argument('--ckpt_interval', type=int, default=None, help='ckpt_interval')
parser.add_argument('--log_interval', type=int, default=100, help='Logging interval steps. Default: 100')
parser.add_argument('--ckpt_path', type=str, default='outputs/', help='Checkpoint save location. Default: outputs/')
parser.add_argument('--ckpt_interval', type=int, default=None, help='Save checkpoint interval. Default: None')
parser.add_argument('--is_save_on_master', type=int, default=1, help='save ckpt on master or all rank')
parser.add_argument('--is_save_on_master', type=int, default=1,
help='Save ckpt on master or all rank, 1 for master, 0 for all ranks. Default: 1')
# distributed related
parser.add_argument('--is_distributed', type=int, default=1, help='if multi device')
parser.add_argument('--rank', type=int, default=0, help='local rank of distributed')
parser.add_argument('--group_size', type=int, default=1, help='world size of distributed')
# roma obs
parser.add_argument('--train_url', type=str, default="", help='train url')
parser.add_argument('--is_distributed', type=int, default=1,
help='Distribute train or not, 1 for yes, 0 for no. Default: 1')
parser.add_argument('--rank', type=int, default=0, help='Local rank of distributed. Default: 0')
parser.add_argument('--group_size', type=int, default=1, help='World size of device. Default: 1')
# profiler init
parser.add_argument('--need_profiler', type=int, default=0, help='whether use profiler')
parser.add_argument('--need_profiler', type=int, default=0,
help='Whether use profiler. 0 for no, 1 for yes. Default: 0')
# reset default config
parser.add_argument('--training_shape', type=str, default="", help='fix training shape')
parser.add_argument('--resize_rate', type=int, default=None, help='resize rate for multi-scale training')
parser.add_argument('--training_shape', type=str, default="", help='Fix training shape. Default: ""')
parser.add_argument('--resize_rate', type=int, default=None,
help='Resize rate for multi-scale training. Default: None')
args, _ = parser.parse_known_args()
if args.lr_scheduler == 'cosine_annealing' and args.max_epoch > args.T_max:
@ -153,7 +157,7 @@ def train():
args.logger.save_args(args)
if args.need_profiler:
from mindinsight.profiler.profiling import Profiler
from mindspore.profiler.profiling import Profiler
profiler = Profiler(output_path=args.outputs_dir, is_detail=True, is_show_op_path=True)
loss_meter = AverageMeter('loss')

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