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test_unet.py
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test_unet.py
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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.utils import test_single_volume_dice, test_single_volume
from trainer import trainer_synapse
from unet import Eff_Unet
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (1, 224, 224), 'pool_size': None,
'crop_pct': .95, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'classifier': 'head',
**kwargs
}
parser = argparse.ArgumentParser()
parser.add_argument('--volume_path', type=str,
default='data/Synapse', 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('--output_dir', type=str, default='exp2',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=120, 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')
args = parser.parse_args()
if args.dataset == "Synapse":
args.volume_path = os.path.join(args.volume_path, "test_vol_h5")
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)
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)
# 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]))
# print(f'idx:{i_batch}, case:{case_name}, mean dice:{np.mean(metric_i, axis=0)[0]}')
logging.info('idx %d case %s mean_dice %f' % (i_batch, case_name, np.mean(metric_i, axis=0)[0]))
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]))
logging.info('Mean class %d mean_dice %f' % (i, metric_list[i-1][0]))
print(f'Mean class:{i}, mean dice:{metric_list[i-1][0]}')
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))
logging.info('Testing performance in best val model: mean_dice : %f.' % (performance))
# print(f'Testing performance (mean_dice): {performance}')
return "Testing Finished!"
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (1, 224, 224), 'pool_size': None,
'crop_pct': .95, 'interpolation': 'bicubic',
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'classifier': 'head',
**kwargs
}
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': './lists/lists_Synapse',
'num_classes': 9,
'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
# net = ViT_seg(config, img_size=args.img_size, num_classes=args.num_classes).cuda()
# net = UNet_hd(n_channels=1, n_classes=args.num_classes, bilinear=True).cuda()
from unet import Eff_Unet
net = Eff_Unet(
layers=[5, 5, 15, 10],
embed_dims=[40, 80, 192, 384],
downsamples=[True, True, True, True],
vit_num=6,
drop_path_rate=0.1,
num_classes=9,
fork_feat=True).cuda()
# for epoch in reversed(range(81,150)):
for epoch in [85]:
# if (epoch+1)%2!=0:
snapshot = os.path.join(args.output_dir, f'epoch_{epoch}.pth')
msg = net.load_state_dict(torch.load(snapshot), strict=True)
snapshot_name = snapshot.split('/')[-1]
# log_folder = f'./test_log/test_log_{args.output_dir}'
case_name = args.output_dir.split('/')[-1]
log_folder = f'test_result/best_epoch_{case_name}'
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)
# 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)