ParaTransCNN/test.py at master · HongkunSun/ParaTransCNN · GitHub

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
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 datasets.dataset_synapse import Synapse_dataset
from datasets.dataset_AVT import AVT_dataset
from utils import test_single_volume_Synapse
from utils import test_single_volume_AVT
from model.ParaTransCNN import ParaTransCNN

parser = argparse.ArgumentParser()
parser.add_argument('--volume_path', type=str,
                    default='', help='root dir for validation volume data')  # for acdc volume_path=root_dir
parser.add_argument('--checkpoint_path', type=str,
                    default='', help='weight root dir for 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('--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='', 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('--batch_size', type=int,
                    default=4, help='batch_size per gpu')
parser.add_argument('--max_epochs', type=int,
                    default=150, help='maximum epoch number to train')
parser.add_argument('--seed', type=int, default=1234, help='random seed')
parser.add_argument('--model_name', type=str,
                    default=" ", help='the name of network')
args = parser.parse_args()


def inference_Synapse(args, model, test_save_path=None):
    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)):
        image, label, case_name = sampled_batch["image"], sampled_batch["label"], sampled_batch['case_name'][0]
        metric_i = test_single_volume_Synapse(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)
        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!"

def inference_AVT(args, model, test_save_path=None):
    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 enumerate(testloader):
        image, label, case_name = sampled_batch["image"], sampled_batch["label"], sampled_batch['case_name'][0]
        origin, direction, xyz_thickness = sampled_batch["origin"], sampled_batch["direction"], sampled_batch["xyz_thickness"]
        origin = origin.detach().numpy()[0]
        direction = direction.detach().numpy()[0]
        xyz_thickness = xyz_thickness.detach().numpy()[0]
        metric_i = test_single_volume_AVT(image, label, model, classes=args.num_classes, patch_size=[args.img_size, args.img_size],
                                      test_save_path=test_save_path, case=case_name, origin=origin, direction=direction, xyz_thickness=xyz_thickness)
        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)
    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,
            "checkpoint_path" : './checkpoints/{}_SGD_{}_{}_{}/epoch_145.pth'.format(args.model_name, args.base_lr,
                                                                               args.max_epochs, args.dataset),
            'list_dir': './lists/lists_Synapse',
            'num_classes': 9,
        },
        'AVT': {
            'Dataset': AVT_dataset,
            "checkpoint_path" : './checkpoints/{}_SGD_{}_{}_{}/epoch_146.pth'.format(args.model_name, args.base_lr,
                                                                               args.max_epochs, args.dataset),
            'list_dir': './lists/lists_AVT',
            'num_classes': 2,
        },
    }
    dataset_name = args.dataset
    args.num_classes = dataset_config[dataset_name]['num_classes']
    args.checkpoint_path = dataset_config[dataset_name]['checkpoint_path']
    args.Dataset = dataset_config[dataset_name]['Dataset']
    args.list_dir = dataset_config[dataset_name]['list_dir']

    net = ParaTransCNN(num_classes=args.num_classes).cuda()
    net.load_state_dict(torch.load(args.checkpoint_path))

    log_folder = args.checkpoint_path
    snapshot_name = log_folder[:-4]
    logging.basicConfig(filename=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 = snapshot_name + "_pre"
        test_save_path = args.test_save_dir
        os.makedirs(test_save_path, exist_ok=True)
    else:
        test_save_path = None
    if args.dataset == 'Synapse':
        inference_Synapse(args, net, test_save_path)
    if args.dataset == 'AVT':
        inference_AVT(args, net, test_save_path)