-
Notifications
You must be signed in to change notification settings - Fork 319
/
test.py
153 lines (137 loc) · 7.78 KB
/
test.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
from os.path import split
import argparse
import logging
import os
import random
import sys
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from tqdm import tqdm
from config import get_config
from datasets.dataset_synapse import Synapse_dataset
from networks.vision_transformer import SwinUnet as ViT_seg
from utils import test_single_volume
parser = argparse.ArgumentParser()
parser.add_argument('--root_path', type=str,
default='../data/Synapse/test_vol_h5',
help='root dir for validation volume data') # for acdc volume_path=root_dir
parser.add_argument('--dataset', type=str,
default='datasets', 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('--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('--img_size', type=int, default=224, help='input patch size of network input')
parser.add_argument('--is_savenii', action="store_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=0.01, help='segmentation network learning rate')
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')
parser.add_argument("--n_class", default=4, type=int)
parser.add_argument("--split_name", default="test", help="Directory of the input list")
args = parser.parse_args()
if args.dataset == "Synapse":
args.volume_path = os.path.join(args.volume_path, "test_vol_h5")
config = get_config(args)
def inference(args, model, test_save_path=None):
db_test = Synapse_dataset(base_dir=args.volume_path, split=args.split_name, list_dir=args.list_dir)
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]
if args.dataset == "datasets":
case_name = split(case_name.split(",")[0])[-1]
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=args.z_spacing)
metric_list += np.array(metric_i)
logging.info('idx %d case %s mean_dice %f mean_hd95 %f' % (
i_batch, case_name, np.mean(metric_i, axis=0)[0], np.mean(metric_i, axis=0)[1]))
metric_list = metric_list / len(db_test)
for i in range(1, args.num_classes):
logging.info('Mean class %d mean_dice %f mean_hd95 %f' % (i, metric_list[i - 1][0], metric_list[i - 1][1]))
performance = np.mean(metric_list, axis=0)[0]
mean_hd95 = np.mean(metric_list, axis=0)[1]
logging.info('Testing performance in best val model: mean_dice : %f mean_hd95 : %f' % (performance, mean_hd95))
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_name = args.dataset
dataset_config = {
args.dataset: {
'root_path': args.root_path,
'list_dir': f'./lists/{args.dataset}',
'num_classes': args.n_class,
"z_spacing": 1
},
}
args.num_classes = dataset_config[dataset_name]['num_classes']
args.volume_path = dataset_config[dataset_name]['root_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
net = ViT_seg(config, img_size=args.img_size, num_classes=args.num_classes).cuda()
snapshot = os.path.join(args.output_dir, 'best_model.pth')
if not os.path.exists(snapshot):
snapshot = snapshot.replace('best_model', 'epoch_' + str(args.max_epochs - 1))
msg = net.load_state_dict(torch.load(snapshot))
print("self trained swin unet", msg)
snapshot_name = snapshot.split('/')[-1]
log_folder = './test_log/test_log_'
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(args.output_dir, "predictions")
test_save_path = args.test_save_dir
os.makedirs(test_save_path, exist_ok=True)
else:
test_save_path = None
inference(args, net, test_save_path)
# python train.py --dataset Synapse --cfg $CFG --root_path $DATA_DIR --max_epochs $EPOCH_TIME --output_dir $OUT_DIR --img_size $IMG_SIZE --base_lr $LEARNING_RATE --batch_size $BATCH_SIZE
# python train.py --output_dir './model_out/datasets' --dataset datasets --img_size 224 --batch_size 32 --cfg configs/swin_tiny_patch4_window7_224_lite.yaml --root_path /media/aicvi/11111bdb-a0c7-4342-9791-36af7eb70fc0/NNUNET_OUTPUT/nnunet_preprocessed/Dataset001_mm/nnUNetPlans_2d_split
# python test.py --output_dir ./model_out/datasets --dataset datasets --cfg configs/swin_tiny_patch4_window7_224_lite.yaml --is_saveni --root_path /media/aicvi/11111bdb-a0c7-4342-9791-36af7eb70fc0/NNUNET_OUTPUT/nnunet_preprocessed/Dataset001_mm/test --max_epoch 150 --base_lr 0.05 --img_size 224 --batch_size 24