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test_synapse.py
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import argparse
import logging
import os
import random
import sys
import numpy as np
from tqdm import tqdm
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
from torch.utils.data import DataLoader
from utils.dataset_synapse import Synapse_dataset
from utils.utils import test_single_volume
from lib.networks import PVT_GCASCADE, MERIT_GCASCADE
parser = argparse.ArgumentParser()
parser.add_argument('--encoder', type=str,
default='PVT', help='Name of encoder: PVT or MERIT')
parser.add_argument('--skip_aggregation', type=str,
default='additive', help='Type of skip-aggregation: additive or concatenation')
parser.add_argument('--volume_path', type=str,
default='./data/synapse/test_vol_h5', help='root dir for validation volume data')
parser.add_argument('--dataset', type=str,
default='Synapse', help='experiment_name')
parser.add_argument('--num_classes', type=int,
default=9, help='output channel of network')
parser.add_argument('--list_dir', type=str,
default='./lists/lists_Synapse', help='list dir')
parser.add_argument('--max_iterations', type=int,default=30000, help='maximum epoch number to train')
parser.add_argument('--max_epochs', type=int, default=300, help='maximum epoch number to train')
parser.add_argument('--batch_size', type=int, default=6,
help='batch_size per gpu')
parser.add_argument('--img_size', type=int, default=224, help='input patch size of network input')
parser.add_argument('--is_savenii', action="store_true", default=True, help='whether to save results during inference')
parser.add_argument('--test_save_dir', type=str, default='predictions', help='saving prediction as nii!')
parser.add_argument('--deterministic', type=int, default=1, help='whether use deterministic training')
parser.add_argument('--base_lr', type=float, default=0.0001, help='segmentation network learning rate')
parser.add_argument('--seed', type=int, default=2222, help='random seed')
args = parser.parse_args()
if(args.num_classes == 14):
classes = ['spleen', 'right kidney', 'left kidney', 'gallbladder', 'esophagus', 'liver', 'stomach', 'aorta', 'inferior vena cava', 'portal vein and splenic vein', 'pancreas', 'right adrenal gland', 'left adrenal gland']
else:
classes = ['spleen', 'right kidney', 'left kidney', 'gallbladder', 'pancreas', 'liver', 'stomach', 'aorta']
def inference(args, model, test_save_path=None):
db_test = args.Dataset(base_dir=args.volume_path, split="test_vol", list_dir=args.list_dir, nclass=args.num_classes)
testloader = DataLoader(db_test, batch_size=1, shuffle=False, num_workers=1)
logging.info("{} test iterations per epoch".format(len(testloader)))
model.eval()
metric_list = 0.0
for i_batch, sampled_batch in tqdm(enumerate(testloader)):
h, w = sampled_batch["image"].size()[2:]
image, label, case_name = sampled_batch["image"], sampled_batch["label"], sampled_batch['case_name'][0]
metric_i = test_single_volume(image, label, model, classes=args.num_classes, patch_size=[args.img_size, args.img_size],
test_save_path=test_save_path, case=case_name, z_spacing=1, class_names=classes)
metric_list += np.array(metric_i)
logging.info('idx %d case %s mean_dice %f mean_hd95 %f, mean_jacard %f mean_asd %f' % (i_batch, case_name, np.mean(metric_i, axis=0)[0], np.mean(metric_i, axis=0)[1], np.mean(metric_i, axis=0)[2], np.mean(metric_i, axis=0)[3]))
metric_list = metric_list / len(db_test)
for i in range(1, args.num_classes):
logging.info('Mean class (%d) %s mean_dice %f mean_hd95 %f, mean_jacard %f mean_asd %f' % (i, classes[i-1], metric_list[i-1][0], metric_list[i-1][1], metric_list[i-1][2], metric_list[i-1][3]))
performance = np.mean(metric_list, axis=0)[0]
mean_hd95 = np.mean(metric_list, axis=0)[1]
mean_jacard = np.mean(metric_list, axis=0)[2]
mean_asd = np.mean(metric_list, axis=0)[3]
logging.info('Testing performance in best val model: mean_dice : %f mean_hd95 : %f, mean_jacard : %f mean_asd : %f' % (performance, mean_hd95, mean_jacard, mean_asd))
return "Testing Finished!"
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_config = {
'Synapse': {
'Dataset': Synapse_dataset,
'volume_path': args.volume_path,
'list_dir': args.list_dir,
'num_classes': args.num_classes,
'z_spacing': 1,
},
}
dataset_name = args.dataset
args.num_classes = dataset_config[dataset_name]['num_classes']
args.volume_path = dataset_config[dataset_name]['volume_path']
args.Dataset = dataset_config[dataset_name]['Dataset']
args.list_dir = dataset_config[dataset_name]['list_dir']
args.z_spacing = dataset_config[dataset_name]['z_spacing']
args.is_pretrain = True
# name the same snapshot defined in train script!
args.exp = 'PVT_GCASCADE_MUTATION_w3_7_Run1_' + dataset_name + str(args.img_size)#+ '_cc_th' +str(args.cc_th) #HR_CASCADERun3_
snapshot_path = "model_pth/{}/{}".format(args.exp, 'PVT_GCASCADE_MUTATION_w3_7_Run1')
snapshot_path = snapshot_path + '_pretrain' if args.is_pretrain else snapshot_path
snapshot_path = snapshot_path + '_' + str(args.max_iterations)[0:2] + 'k' if args.max_iterations != 30000 else snapshot_path
snapshot_path = snapshot_path + '_epo' + str(args.max_epochs) if args.max_epochs != 30 else snapshot_path
snapshot_path = snapshot_path+'_bs'+str(args.batch_size)
snapshot_path = snapshot_path + '_lr' + str(args.base_lr) if args.base_lr != 0.01 else snapshot_path
snapshot_path = snapshot_path + '_'+str(args.img_size)
snapshot_path = snapshot_path + '_s'+str(args.seed) if args.seed!=1234 else snapshot_path
if args.encoder=='PVT':
net = PVT_GCASCADE(n_class=args.num_classes, img_size=args.img_size, k=11, padding=5, conv='mr', gcb_act='gelu', skip_aggregation=args.skip_aggregation)
elif args.encoder=='MERIT':
net = MERIT_GCASCADE(n_class=args.num_classes, img_size_s1=(args.img_size,args.img_size), img_size_s2=(224,224), k=11, padding=5, conv='mr', gcb_act='gelu', skip_aggregation=args.skip_aggregation)
else:
print('Implementation not found for this encoder. Exiting!')
sys.exit()
snapshot = os.path.join(snapshot_path, 'best.pth')
if not os.path.exists(snapshot): snapshot = snapshot.replace('best', 'epoch_'+str(args.max_epochs-1))
net.load_state_dict(torch.load(snapshot))
snapshot_name = snapshot_path.split('/')[-1]
log_folder = 'test_log/test_log_' + args.exp
os.makedirs(log_folder, exist_ok=True)
logging.basicConfig(filename=log_folder + '/'+snapshot_name+".txt", level=logging.INFO, format='[%(asctime)s.%(msecs)03d] %(message)s', datefmt='%H:%M:%S')
logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
logging.info(str(args))
logging.info(snapshot_name)
if args.is_savenii:
args.test_save_dir = os.path.join(snapshot_path, "predictions")
test_save_path = os.path.join(args.test_save_dir, args.exp, snapshot_name+'2')
os.makedirs(test_save_path, exist_ok=True)
else:
test_save_path = None
inference(args, net, test_save_path)