-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest.py
175 lines (153 loc) · 8.98 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
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
import argparse
import logging
import os
import random
import sys
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm
from datasets.dataset_synapse import Synapse_dataset
from utils import test_single_volume
from networks.vit_seg_modeling import VisionTransformer as ViT_seg
from networks.vit_seg_modeling import CONFIGS as CONFIGS_ViT_seg
from networks.Vit_new import VisionTransformer as conv_ViT_seg
from networks.mlvit import MLViTSeg
from networks.Seg import MLPmedical
from send_email import sendEmailText
torch.cuda.set_device(0)
print(torch.cuda.current_device())
parser = argparse.ArgumentParser()
parser = argparse.ArgumentParser()
parser.add_argument('--volume_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='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('--n_gpu', type=int, default=1, help='total gpu')
parser.add_argument('--max_iterations', type=int,default=20000, 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=16,
help='batch_size per gpu')
parser.add_argument('--img_size', type=int, default=384, 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('--n_skip', type=int, default=3, help='using number of skip-connect, default is num')
parser.add_argument('--vit_name', type=str, default='ViT-B_16', help='select one vit model')
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('--vit_patches_size', type=int, default=16, help='vit_patches_size, default is 16')
parser.add_argument('--local_rank', default=1, type=int,help='local_rank')
args = parser.parse_args()
def inference(args, model, test_save_path=None,n_gpu = None):
if n_gpu >1:
db_test = args.Dataset(base_dir=args.volume_path, split="test_vol", list_dir=args.list_dir)
tset_sampler = torch.utils.data.distributed.DistributedSampler(db_test,shuffle = True)
testloader = torch.utils.data.DataLoader(db_test, batch_size=1, sampler=tset_sampler)
db_test = args.Dataset(base_dir=args.volume_path, split="test_vol", 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]
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)
p = ''
for i in range(1, args.num_classes):
p = p + 'cls%d: dice %f hd95 %f, '%(i, metric_i[i-1][0], metric_i[i-1][1])
logging.info('\nidx %d case %s %s mean_dice %f mean_hd95 %f' % (i_batch, case_name, p, np.mean(metric_i, axis=0)[0], np.mean(metric_i, axis=0)[1]))
#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_config = {
'Synapse': {
'Dataset': Synapse_dataset,
'volume_path': '/home/kkk/medical/snp/project_TransUNet/data/Synapse/test_vol_h5',
'list_dir': '/home/kkk/medical/snp/project_TransUNet/TransUNet/lists/lists_Synapse',
'num_classes': 9,
'z_spacing': 1,
},
}
dataset_name = args.dataset
if args.num_classes == 0:
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 = 'TU_' + dataset_name + str(args.img_size)
snapshot_path = "../model/{}/{}".format(args.exp, 'TU')
snapshot_path = snapshot_path + '_pretrain' if args.is_pretrain else snapshot_path
snapshot_path += '_' + args.vit_name
snapshot_path = snapshot_path + '_skip' + str(args.n_skip)
snapshot_path = snapshot_path + '_vitpatch' + str(args.vit_patches_size) if args.vit_patches_size!=16 else snapshot_path
snapshot_path = snapshot_path + '_epo' + str(args.max_epochs) if args.max_epochs != 30 else snapshot_path
if dataset_name == 'ACDC': # using max_epoch instead of iteration to control training duration
snapshot_path = snapshot_path + '_' + str(args.max_iterations)[0:2] + 'k' if args.max_iterations != 30000 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
config_vit = CONFIGS_ViT_seg[args.vit_name]
config_vit.n_classes = args.num_classes
#config_vit.n_skip = args.n_skip
config_vit.patches.size = (args.vit_patches_size, args.vit_patches_size)
if args.vit_name.find('R50') !=-1:
config_vit.patches.grid = (int(args.img_size/args.vit_patches_size), int(args.img_size/args.vit_patches_size))
#net = ViT_seg(config_vit, img_size=args.img_size, num_classes=config_vit.n_classes).cuda()
net = MLPmedical(config_vit).cuda()
#net = conv_ViT_seg(config_vit, img_size=args.img_size, num_classes=config_vit.n_classes).cuda()
snapshot = os.path.join(snapshot_path, 'best_model.pth')
snapshot = "/home/kkk/medical/model/TU_Synapse384/TU_pretrain_R50-ViT-B_16_HS-MLPv2_without pretrain_skip3_epo500_bs5_lr0.0003_384/epoch_499.pth"
if not os.path.exists(snapshot): snapshot = snapshot.replace('best_model', 'epoch_'+str(args.max_epochs-1))
#net.load_state_dict(torch.load(snapshot))
if args.n_gpu > 1:
device_ids = [0,1,2,3]
torch.distributed.init_process_group(backend='nccl', init_method='env://', rank=0, world_size=1)
model = torch.nn.parallel.DistributedDataParallel(net, device_ids=device_ids, find_unused_parameters=True)
model.load_state_dict(torch.load(snapshot),False)
net = model.module
else:
net.load_state_dict(torch.load(snapshot),False)
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 = '../predictions'
test_save_path = "/home/kkk/medical/predictions/"
# os.makedirs(test_save_path, exist_ok=True)
else:
test_save_path = None
inference(args, net, test_save_path,n_gpu=args.n_gpu)
#sendEmailText('code done','[email protected]','sfjlmvqiktdfbggd','[email protected]','Hi!\nYour testing code is finished.\n','smtp.qq.com')