def Argments():
parser = argparse.ArgumentParser(
description='Single Shot MultiBox Detector Training With Pytorch')
# Train params
parser.add_argument('--batch_size',
default=33,
type=int,
help='Batch size for training')
parser.add_argument('--num_epochs',
default=121,
type=int,
help='the number epochs')
parser.add_argument('--num_workers',
default=16,
type=int,
help='Number of workers used in dataloading')
parser.add_argument('--config',
default='config/prague_combine_balance.yaml',
help='configuration')
args = parser.parse_args()
print(args)
configuration = load_model_configuration(args.config)
configuration["flow_control"] = {}
variable_dict = vars(args)
for key in variable_dict.keys():
configuration["flow_control"][key] = variable_dict[key]
return configuration
def Arguments():
parser = argparse.ArgumentParser(
description="SSD Evaluation on VOC Dataset.")
parser.add_argument(
'--net',
default="vgg16-ssd",
help=
"The network architecture, it should be of mb1-ssd, mb1-ssd-lite, mb2-ssd-lite or vgg16-ssd."
)
parser.add_argument("--trained_model", type=str)
parser.add_argument(
"--dataset_type",
default="voc",
type=str,
help='Specify dataset type. Currently support voc and open_images.')
parser.add_argument(
"--dataset",
type=str,
help=
"The root directory of the VOC dataset or Open Images dataset or coco dataset or ecp dataset."
)
parser.add_argument("--label_file", type=str, help="The label file path.")
parser.add_argument("--use_cuda", type=str2bool, default=True)
parser.add_argument("--use_2007_metric", type=str2bool, default=True)
parser.add_argument("--nms_method", type=str, default="hard")
parser.add_argument("--iou_threshold",
type=float,
default=0.5,
help="The threshold of Intersection over Union.")
parser.add_argument("--eval_dir",
default="../experiments/eval_results",
type=str,
help="The directory to store evaluation results.")
parser.add_argument('--mb2_width_mult',
default=1.0,
type=float,
help='Width Multiplifier for MobilenetV2')
parser.add_argument('--config',
default='config/default_setting.yaml',
type=str,
help='Configuration')
args = parser.parse_args()
print(args)
configuration = load_model_configuration(args.config)
configuration["flow_control"] = {}
variable_dict = vars(args)
for key in variable_dict.keys():
configuration['flow_control'][key] = variable_dict[key]
return configuration
def Argments():
parser = argparse.ArgumentParser(
description='Single Shot MultiBox Detector Training With Pytorch')
parser.add_argument(
"--dataset_type",
default="voc",
type=str,
help=
'Specify dataset type. Currently support voc, open_images, ecp, ecp-random and ecp-centroid.'
)
parser.add_argument(
'--net',
default="vgg17-ssd",
help=
"The network architecture, it can be mb2-ssd, mb1-lite-ssd, mb2-ssd-lite or vgg16-ssd."
)
parser.add_argument('--freeze_base_net',
action='store_true',
help="Freeze base net layers.")
parser.add_argument(
'--freeze_net',
action='store_true',
help="Freeze all the layers except the prediction head.")
# Params for SGD
# Params for loading pretrained basenet or checkpoints.
parser.add_argument('--base_net', help='Pretrained base model')
parser.add_argument('--pretrained_ssd', help='Pre-trained base model')
parser.add_argument(
'--resume',
default=None,
type=str,
help='Checkpoint state_dict file to resume training from')
# Train params
parser.add_argument('--batch_size',
default=33,
type=int,
help='Batch size for training')
parser.add_argument('--num_epochs',
default=121,
type=int,
help='the number epochs')
parser.add_argument('--num_workers',
default=16,
type=int,
help='Number of workers used in dataloading')
parser.add_argument('--validation_epochs',
default=6,
type=int,
help='the number epochs')
parser.add_argument('--debug_steps',
default=101,
type=int,
help='Set the debug log output frequency.')
parser.add_argument('--use_cuda',
default=True,
type=bool,
help='Use CUDA to train model')
parser.add_argument('--checkpoint_folder',
default='../experiments/models',
type=str,
help='Directory for saving checkpoint models')
parser.add_argument('--config',
default='config/default_setting.yaml',
help='configuration')
parser.add_argument('--dataset_ratio',
default=0.1,
help="Initial set partial dataset ratio")
parser.add_argument('--sample_method',
type=str,
default='random',
help="random, sequencial, uncertainty")
args = parser.parse_args()
print(args)
configuration = load_model_configuration(args.config)
configuration["flow_control"] = {}
variable_dict = vars(args)
for key in variable_dict.keys():
configuration["flow_control"][key] = variable_dict[key]
return configuration
