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train.py
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train.py
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import argparse
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from models.HiFormer import HiFormer
import configs.HiFormer_configs as configs
from trainer import trainer
from utils import random_split_array
parser = argparse.ArgumentParser()
parser.add_argument('--root_path', type=str,
default='./data/Synapse/train_npz', help='root dir for data')
parser.add_argument('--num_classes', type=int,
default=2, help='output channel of network')
parser.add_argument('--max_iterations', type=int,
default=30000, help='maximum epoch number to train')
parser.add_argument('--max_epochs', type=int,
default=401, help='maximum epoch number to train')
parser.add_argument('--batch_size', type=int,
default=10, help='batch_size per gpu')
parser.add_argument('--base_lr', type=float, default=0.01,
help='segmentation network learning rate')
parser.add_argument('--num_workers', type=int, default=2,
help='number of workers')
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('--output_dir', type=str,
default='./results', help='root dir for output log')
parser.add_argument('--model_name', type=str,
default='hiformer-b', help='[hiformer-s, hiformer-b, hiformer-l]')
parser.add_argument('--eval_interval', type=int,
default=20, help='evaluation epoch')
parser.add_argument('--is_liver', action='store_true',
default=0, help='add for liver, remove for tumor')
parser.add_argument('--n_gpu', type=int, default=1, help='total gpu')
args = parser.parse_args()
args.output_dir = args.output_dir + f'/{args.model_name}'
os.makedirs(args.output_dir, exist_ok=True)
if __name__ == "__main__":
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)
CONFIGS = {
'hiformer-s': configs.get_hiformer_s_configs(),
'hiformer-b': configs.get_hiformer_b_configs(),
'hiformer-l': configs.get_hiformer_l_configs(),
}
if args.batch_size != 24 and args.batch_size % 6 == 0:
args.base_lr *= args.batch_size / 24
model = HiFormer(config=CONFIGS[args.model_name], img_size=args.img_size, n_classes=args.num_classes).cuda()
if (args.is_liver):
organ = "liver"
else:
organ = "cancer"
#Splitting Dataset
original = []
for i in range(131) :
original.append(i)
train, test, val = random_split_array(original,(0.8,0.1,0.1))
print("SEED: ",args.seed)
print("Training Folders - ")
print(train)
print("Testing Folders - ")
print(test)
print("Validation Folders - ")
print(val)
X_train = []
Y_train = []
X_test = []
Y_test = []
X_val = []
Y_val = []
scan_list = os.listdir(args.root_path)
scan_list.sort()
for i in scan_list:
num = int(i.split("_")[-1])
path = os.path.join(args.root_path,i)
imgpath = os.path.join(path,"images")
maskpath = os.path.join(path,"masks")
piclist = os.listdir(imgpath)
if num in train:
for j in piclist:
X_train.append(os.path.join(imgpath,j))
Y_train.append(os.path.join(os.path.join(maskpath,organ),j))
elif num in test:
for j in piclist:
X_test.append(os.path.join(imgpath,j))
Y_test.append(os.path.join(os.path.join(maskpath,organ),j))
else:
for j in piclist:
X_val.append(os.path.join(imgpath,j))
Y_val.append(os.path.join(os.path.join(maskpath,organ),j))
print(len(X_train))
print("Train length : ", len(X_train))
print("Test length : ", len(X_test))
print("Val length : ", len(X_val))
trainer(args, model, args.output_dir, X_train, Y_train, X_val, Y_val)