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test.py
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test.py
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import argparse
import os
import time
import random
import numpy as np
import setproctitle
import torch
import torch.backends.cudnn as cudnn
cudnn.benchmark = True
import torch.optim
from torch.utils.data import DataLoader
from data.BraTS import BraTS
from predict import validate_softmax
from models.TransBTS.TransBTS_downsample8x_skipconnection import TransBTS
parser = argparse.ArgumentParser()
parser.add_argument('--user', default='name of user', type=str)
parser.add_argument('--root', default='path to testing set', type=str)
parser.add_argument('--valid_dir', default='Valid', type=str)
parser.add_argument('--valid_file', default='valid.txt', type=str)
parser.add_argument('--output_dir', default='output', type=str)
parser.add_argument('--submission', default='submission', type=str)
parser.add_argument('--visual', default='visualization', type=str)
parser.add_argument('--experiment', default='', type=str)
parser.add_argument('--test_date', default='', type=str)
parser.add_argument('--test_file', default='', type=str)
parser.add_argument('--use_TTA', default=True, type=bool)
parser.add_argument('--post_process', default=True, type=bool)
parser.add_argument('--save_format', default='nii', choices=['npy', 'nii'], type=str)
parser.add_argument('--crop_H', default=128, type=int)
parser.add_argument('--crop_W', default=128, type=int)
parser.add_argument('--crop_D', default=128, type=int)
parser.add_argument('--seed', default=1000, type=int)
parser.add_argument('--model_name', default='TransBTS', type=str)
parser.add_argument('--num_class', default=4, type=int)
parser.add_argument('--no_cuda', default=False, type=bool)
parser.add_argument('--gpu', default='0', type=str)
parser.add_argument('--num_workers', default=4, type=int)
args = parser.parse_args()
def main():
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
_, model = TransBTS(dataset='brats', _conv_repr=True, _pe_type="learned")
model = torch.nn.DataParallel(model).cuda()
load_file = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'checkpoint', args.experiment+args.test_date, args.test_file)
if os.path.exists(load_file):
checkpoint = torch.load(load_file)
model.load_state_dict(checkpoint['state_dict'])
args.start_epoch = checkpoint['epoch']
print('Successfully load checkpoint {}'.format(os.path.join(args.experiment+args.test_date, args.test_file)))
else:
print('There is no resume file to load!')
valid_list = os.path.join(args.root, args.valid_dir, args.valid_file)
valid_root = os.path.join(args.root, args.valid_dir)
valid_set = BraTS(valid_list, valid_root, mode='test')
print('Samples for valid = {}'.format(len(valid_set)))
valid_loader = DataLoader(valid_set, batch_size=1, shuffle=False, num_workers=args.num_workers, pin_memory=True)
submission = os.path.join(os.path.abspath(os.path.dirname(__file__)), args.output_dir,
args.submission, args.experiment+args.test_date)
visual = os.path.join(os.path.abspath(os.path.dirname(__file__)), args.output_dir,
args.visual, args.experiment+args.test_date)
if not os.path.exists(submission):
os.makedirs(submission)
if not os.path.exists(visual):
os.makedirs(visual)
start_time = time.time()
with torch.no_grad():
validate_softmax(valid_loader=valid_loader,
model=model,
load_file=load_file,
multimodel=False,
savepath=submission,
visual=visual,
names=valid_set.names,
use_TTA=args.use_TTA,
save_format=args.save_format,
snapshot=True,
postprocess=True
)
end_time = time.time()
full_test_time = (end_time-start_time)/60
average_time = full_test_time/len(valid_set)
print('{:.2f} minutes!'.format(average_time))
if __name__ == '__main__':
# config = opts()
setproctitle.setproctitle('{}: Testing!'.format(args.user))
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
assert torch.cuda.is_available(), "Currently, we only support CUDA version"
main()