99 lines
4.2 KiB
Python
99 lines
4.2 KiB
Python
|
import argparse
|
||
|
|
import logging
|
||
|
|
import os
|
||
|
|
import random
|
||
|
|
import numpy as np
|
||
|
|
import torch
|
||
|
|
import torch.backends.cudnn as cudnn
|
||
|
|
from networks.vision_transformer import SwinUnet as ViT_seg
|
||
|
|
from trainer import trainer_synapse
|
||
|
|
from config import get_config
|
||
|
|
|
||
|
|
parser = argparse.ArgumentParser()
|
||
|
|
parser.add_argument('--root_path', type=str,
|
||
|
|
default='../data/Synapse/train_npz', help='root dir for data')
|
||
|
|
parser.add_argument('--dataset', type=str,
|
||
|
|
default='Synapse', help='experiment_name')
|
||
|
|
parser.add_argument('--list_dir', type=str,
|
||
|
|
default='./lists/lists_Synapse', help='list dir')
|
||
|
|
parser.add_argument('--num_classes', type=int,
|
||
|
|
default=9, help='output channel of network')
|
||
|
|
parser.add_argument('--output_dir', type=str, help='output dir')
|
||
|
|
parser.add_argument('--max_iterations', type=int,
|
||
|
|
default=30000, help='maximum epoch number to train')
|
||
|
|
parser.add_argument('--max_epochs', type=int,
|
||
|
|
default=150, help='maximum epoch number to train')
|
||
|
|
parser.add_argument('--batch_size', type=int,
|
||
|
|
default=24, help='batch_size per gpu')
|
||
|
|
parser.add_argument('--n_gpu', type=int, default=1, help='total gpu')
|
||
|
|
parser.add_argument('--deterministic', type=int, default=1,
|
||
|
|
help='whether use deterministic training')
|
||
|
|
parser.add_argument('--base_lr', type=float, default=0.01,
|
||
|
|
help='segmentation network learning rate')
|
||
|
|
parser.add_argument('--img_size', type=int,
|
||
|
|
default=224, help='input patch size of network input')
|
||
|
|
parser.add_argument('--seed', type=int,
|
||
|
|
default=1234, help='random seed')
|
||
|
|
parser.add_argument('--cfg', type=str, required=True, metavar="FILE", help='path to config file', )
|
||
|
|
parser.add_argument(
|
||
|
|
"--opts",
|
||
|
|
help="Modify config options by adding 'KEY VALUE' pairs. ",
|
||
|
|
default=None,
|
||
|
|
nargs='+',
|
||
|
|
)
|
||
|
|
parser.add_argument('--zip', action='store_true', help='use zipped dataset instead of folder dataset')
|
||
|
|
parser.add_argument('--cache-mode', type=str, default='part', choices=['no', 'full', 'part'],
|
||
|
|
help='no: no cache, '
|
||
|
|
'full: cache all data, '
|
||
|
|
'part: sharding the dataset into nonoverlapping pieces and only cache one piece')
|
||
|
|
parser.add_argument('--resume', help='resume from checkpoint')
|
||
|
|
parser.add_argument('--accumulation-steps', type=int, help="gradient accumulation steps")
|
||
|
|
parser.add_argument('--use-checkpoint', action='store_true',
|
||
|
|
help="whether to use gradient checkpointing to save memory")
|
||
|
|
parser.add_argument('--amp-opt-level', type=str, default='O1', choices=['O0', 'O1', 'O2'],
|
||
|
|
help='mixed precision opt level, if O0, no amp is used')
|
||
|
|
parser.add_argument('--tag', help='tag of experiment')
|
||
|
|
parser.add_argument('--eval', action='store_true', help='Perform evaluation only')
|
||
|
|
parser.add_argument('--throughput', action='store_true', help='Test throughput only')
|
||
|
|
|
||
|
|
args = parser.parse_args()
|
||
|
|
if args.dataset == "Synapse":
|
||
|
|
args.root_path = os.path.join(args.root_path, "train_npz")
|
||
|
|
config = get_config(args)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
if not args.deterministic:
|
||
|
|
cudnn.benchmark = True
|
||
|
|
cudnn.deterministic = False
|
||
|
|
else:
|
||
|
|
cudnn.benchmark = False
|
||
|
|
cudnn.deterministic = True
|
||
|
|
|
||
|
|
random.seed(args.seed)
|
||
|
|
np.random.seed(args.seed)
|
||
|
|
torch.manual_seed(args.seed)
|
||
|
|
torch.cuda.manual_seed(args.seed)
|
||
|
|
|
||
|
|
dataset_name = args.dataset
|
||
|
|
dataset_config = {
|
||
|
|
'Synapse': {
|
||
|
|
'root_path': args.root_path,
|
||
|
|
'list_dir': './lists/lists_Synapse',
|
||
|
|
'num_classes': 9,
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
if args.batch_size != 24 and args.batch_size % 6 == 0:
|
||
|
|
args.base_lr *= args.batch_size / 24
|
||
|
|
args.num_classes = dataset_config[dataset_name]['num_classes']
|
||
|
|
args.root_path = dataset_config[dataset_name]['root_path']
|
||
|
|
args.list_dir = dataset_config[dataset_name]['list_dir']
|
||
|
|
|
||
|
|
if not os.path.exists(args.output_dir):
|
||
|
|
os.makedirs(args.output_dir)
|
||
|
|
net = ViT_seg(config, img_size=args.img_size, num_classes=args.num_classes).cuda()
|
||
|
|
net.load_from(config)
|
||
|
|
|
||
|
|
trainer = {'Synapse': trainer_synapse,}
|
||
|
|
trainer[dataset_name](args, net, args.output_dir)
|