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option.py
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import argparse
import os
fcn_resume = None
pspnet_resume = None
deeplabv3_resum = None
class Options():
def __init__(self):
parser = argparse.ArgumentParser(description='PaddlePaddle Segmentation')
# model and dataset
parser.add_argument('--model', type=str, default='fcn',
help='model name (default: fcn)')
parser.add_argument('--backbone', type=str, default='resnet50',
help='backbone name (default: resnet50)')
parser.add_argument('--backbone-style', type=str, default='pytorch',
help='backbone name (default: pytorch)')
parser.add_argument('--pu', type=str, default=
None, help='Parallel Unit')
parser.add_argument('--dilated', action='store_true', default=
True, help='dilation')
parser.add_argument('--lateral', action='store_true', default=
False, help='employ FPN')
parser.add_argument('--reader', type=str, default='voc',
help='reader name (default: voc)')
parser.add_argument('--data-path', type=str, default=r"E:\Data\datasets",
help='reader path')
parser.add_argument('--checkpoints-path', type=str, default=None,
help='checkpoints path')
parser.add_argument('--workers', type=int, default=16,
metavar='N', help='dataloader threads')
parser.add_argument('--base-size', type=int, default=None,
help='base image size')
parser.add_argument('--crop-size', type=int, default=None,
help='crop image size')
parser.add_argument('--train-split', type=str, default='train',
help='dataset train split (default: train)')
# training hyper params
parser.add_argument('--aux', action='store_true', default=True,
help='Auxilary Loss')
parser.add_argument('--aux-weight', type=float, default=None,
help='Auxilary loss weight (default: 0.2)')
parser.add_argument('--se-loss', action='store_true', default=False,
help='Semantic Encoding Loss SE-loss')
parser.add_argument('--se-weight', type=float, default=0.2,
help='SE-loss weight (default: 0.2)')
parser.add_argument('--epochs', type=int, default=None, metavar='N',
help='number of epochs to train (default: auto)')
parser.add_argument('--start_epoch', type=int, default=0,
metavar='N', help='start epochs (default:0)')
parser.add_argument('--batch-size', type=int, default=None,
metavar='N', help='input batch size for \
training (default: auto)')
parser.add_argument('--test-batch-size', type=int, default=None,
metavar='N', help='input batch size for \
testing (default: same as batch size)')
# optimizer params
parser.add_argument('--lr', type=float, default=None, metavar='LR',
help='learning rate (default: auto)')
parser.add_argument('--lr-scheduler', type=str, default='poly',
help='learning rate scheduler (default: poly)')
parser.add_argument('--momentum', type=float, default=0.9,
metavar='M', help='momentum (default: 0.9)')
parser.add_argument('--weight-decay', type=float, default=1e-4,
metavar='M', help='w-decay (default: 1e-4)')
# cuda, seed and logging
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
# checking point
parser.add_argument('--resume', type=bool, default=False,
help='put the path to resuming file if needed')
parser.add_argument('--resume-path', type=str, default=None,
help='put the path to resuming file if needed')
# finetuning pre-trained models
parser.add_argument('--ft', type=bool, default=False,
help='finetuning on a different dataset')
# evaluation option
parser.add_argument('--split', default='val')
parser.add_argument('--mode', default='testval')
parser.add_argument('--ms', action='store_true', default=False,
help='multi scale & flip')
parser.add_argument('--no-val', action='store_true', default=False,
help='skip validation during training')
parser.add_argument('--save-folder', type=str, default='results',
help='path to save images')
# logger
parser.add_argument('--logger-folder', type=str, default=None,
help='path to save images')
# parallel unit configuration
parser.add_argument('--dilations', type=tuple, default=(1,2,4,8),
help='dilations')
# graph size
parser.add_argument('--graphs', type=tuple, default=(256,128,64,32),
help='dilations')
# the parser
self.parser = parser
def parse(self):
args = self.parser.parse_args()
# default settings for epochs, batch_size and lr
if args.epochs is None:
epoches = {
'coco': 30,
'citys': 240,
'voc': 50,
'voc_aug': 50,
'pcontext': 80,
'ade20k': 120,
}
args.epochs = epoches[args.reader.lower()]
if args.batch_size is None:
batch_size = {
'coco': 8,
'citys': 8,
'voc': 1,
'voc_aug': 8,
'pcontext': 8,
'ade20k': 8,
}
args.batch_size = batch_size[args.reader.lower()]
if args.test_batch_size is None:
args.test_batch_size = args.batch_size
if args.lr is None:
lrs = {
'coco': 0.01,
'citys': 0.01,
'voc': 0.0001,
'voc_aug': 0.001,
'pcontext': 0.001,
'ade20k': 0.01,
}
args.lr = lrs[args.reader.lower()] / 16 * args.batch_size
if args.checkpoints_path is None:
if args.dilations is not None and args.model == "grnet":
args.checkpoints_path = f"./work/{args.model}_{args.backbone}-{args.backbone_style}_{args.reader}_{args.pu}-{args.dilations}_graphs-{args.graphs}_checkpoint.pkl"
elif args.dilations is not None and args.model != "grnet":
args.checkpoints_path = f"./work/{args.model}_{args.backbone}-{args.backbone_style}_{args.reader}_{args.pu}-{args.dilations}_checkpoint.pkl"
else:
args.checkpoints_path = f"./work/{args.model}_{args.backbone}-{args.backbone_style}_{args.reader}_checkpoint.pkl"
if args.resume and args.ft:
args.resume_path = args.checkpoints_path.replace("voc", "voc_aug")
else:
args.resume_path = args.checkpoints_path
if args.logger_folder is None:
args.logger_folder = "./work/log"
if not os.path.exists(args.logger_folder):
os.makedirs(args.logger_folder)
if args.base_size is None or args.crop_size is None:
size = {
"coco": (520, 512),
"citys": (1024, 800),
"voc": (520, 512),
"voc_aug": (520, 512),
"pcontext": (520, 512),
"ade20k": (520, 512)
}
args.base_size, args.crop_size = size[args.reader.lower()]
print(args)
return args
if __name__ == '__main__':
option = Options()
args = option.parse()
print(args.resume)
print(args.ft)
print(args.reader)
print(args.checkpoints_path)
print(args.logger_folder)