def test_calculate_metric(epoch_num,
patch_size=(128, 128, 64),
stride_xy=64,
stride_z=32,
device='cuda'):
net = VNet(n_channels=1,
n_classes=num_classes,
normalization='batchnorm',
has_dropout=False).to(device)
save_mode_path = os.path.join(snapshot_path,
'iter_' + str(epoch_num) + '.pth')
print(save_mode_path)
net.load_state_dict(torch.load(save_mode_path))
print("init weight from {}".format(save_mode_path))
net.eval()
metrics = test_all_case(net,
image_list,
num_classes=num_classes,
name_classes=name_classes,
patch_size=patch_size,
stride_xy=stride_xy,
stride_z=stride_z,
save_result=True,
test_save_path=test_save_path,
device=device)
return metrics
def test_calculate_metric(epoch_num):
net = VNet(n_channels=1, n_classes=num_classes-1, normalization='batchnorm', has_dropout=False).cuda()
save_mode_path = os.path.join(snapshot_path, 'iter_' + str(epoch_num) + '.pth')
net.load_state_dict(torch.load(save_mode_path))
print("init weight from {}".format(save_mode_path))
net.eval()
avg_metric = test_all_case(net, image_list, num_classes=num_classes,
patch_size=(112, 112, 80), stride_xy=18, stride_z=4,
save_result=True, test_save_path=test_save_path,
metric_detail=FLAGS.detail, nms=FLAGS.nms)
return avg_metric
def test_calculate_metric(
model_path, patch_size=(128, 128, 64),
stride_xy=64, stride_z=32,
device='cuda'):
#net = VNet(n_channels=1, n_classes=num_classes, normalization='batchnorm', has_dropout=False).to(device)
#net.load_state_dict(torch.load(model_path))
net = torch.load(model_path)
net.eval()
metrics = test_all_case(
net, image_list,
num_classes=num_classes,
name_classes=name_classes,
patch_size=patch_size, stride_xy=stride_xy, stride_z=stride_z,
save_result=True, test_save_path=test_save_path)
return metrics
def test_calculate_metric():
net = VNet(n_channels=1,
n_classes=num_classes,
normalization='batchnorm',
has_dropout=False).cuda()
save_mode_path = FLAGS.model
net.load_state_dict(torch.load(save_mode_path))
print("init weight from {}".format(save_mode_path))
net.eval()
avg_metric = test_all_case(net,
image_list,
num_classes=num_classes,
patch_size=(112, 112, 80),
stride_xy=18,
stride_z=4,
save_result=True,
test_save_path=test_save_path)
return avg_metric
def Inference(FLAGS):
snapshot_path = "../model/{}/{}".format(FLAGS.exp, FLAGS.model)
num_classes = 2
test_save_path = "../model/BraTs2019_Mean_Teacher_25/{}_Prediction".format(
FLAGS.model)
if os.path.exists(test_save_path):
shutil.rmtree(test_save_path)
os.makedirs(test_save_path)
net = unet_3D(n_classes=num_classes, in_channels=1).cuda()
save_mode_path = os.path.join(snapshot_path,
'{}_best_model.pth'.format(FLAGS.model))
net.load_state_dict(torch.load(save_mode_path))
print("init weight from {}".format(save_mode_path))
net.eval()
avg_metric = test_all_case(net,
base_dir=FLAGS.root_path,
method=FLAGS.model,
test_list="test.txt",
num_classes=num_classes,
patch_size=(96, 96, 96),
stride_xy=64,
stride_z=64,
test_save_path=test_save_path)
return avg_metric