def test(model, args):
data_path = args.data_path
gpu = args.gpu
n_classes = args.classes
data_width = args.width
data_height = args.height
# set device configuration
device_ids = []
if gpu == 'gpu':
if not torch.cuda.is_available():
print("No cuda available")
raise SystemExit
device = torch.device(args.device1)
device_ids.append(args.device1)
if args.device2 != -1:
device_ids.append(args.device2)
if args.device3 != -1:
device_ids.append(args.device3)
if args.device4 != -1:
device_ids.append(args.device4)
else:
device = torch.device("cpu")
if len(device_ids) > 1:
model = nn.DataParallel(model, device_ids=device_ids)
model = model.to(device)
# set testdataset
test_dataset = SampleDataset(data_path)
test_loader = DataLoader(
test_dataset,
batch_size=10,
num_workers=4,
)
print('test_dataset : {}, test_loader : {}'.format(len(test_dataset),
len(test_loader)))
avg_score = 0.0
# test
model.eval() # Set model to evaluate mode
with torch.no_grad():
for batch_idx, (inputs, labels) in enumerate(test_loader):
inputs = inputs.to(device).float()
labels = labels.to(device).long()
target = make_one_hot(labels[:, 0, :, :], n_classes, device)
pred = model(inputs)
loss = dice_score(pred, target)
avg_score += loss.data.cpu().numpy()
del inputs, labels, target, pred, loss
avg_score /= len(test_loader)
print('dice_score : {:.4f}'.format(avg_score))
from loss import dice_score
torch.backends.cudnn.benchmark = True
torch.manual_seed(10)
file_path = './brats18_dataset/npy_test/test_t2.npy'
model_path = './weight/unet_4.pth'
train_data = Dataset_brain_4(file_path)
batch_size = 64
train_loader = data.DataLoader(dataset=train_data,
batch_size=batch_size,
num_workers=4)
unet = Unet(4)
unet.load_state_dict(torch.load(model_path))
unet.cuda()
unet.eval()
batch_score = 0
num_batch = 0
with torch.no_grad():
for i, (img, label) in enumerate(train_loader):
# print(img.shape)
seg = unet(img.float().cuda())
seg = seg.cpu()
seg[seg >= 0.5] = 1.
seg[seg != 1] = 0.
batch_score += dice_score(seg, label.float()).data.numpy()
# print(num_batch)
num_batch += img.size(0)
del seg, img, label
batch_score /= num_batch
print(' test_score = %.4f' % (batch_score))
def test(model, args):
data_path = args.data_path
n_channels = args.channels
n_classes = args.classes
data_width = args.width
data_height = args.height
gpu = args.gpu
# Hyper paremter for MagNet
thresholds = [0.01, 0.05, 0.001, 0.005]
reformer_model = None
if args.reformer == 'autoencoder1':
reformer_model = autoencoder(n_channels)
elif args.reformer == 'autoencoder2':
reformer_model = autoencoder2(n_channels)
else:
print("wrong reformer model : must be autoencoder1 or autoencoder2")
raise SystemExit
print('reformer model')
summary(reformer_model,
input_size=(n_channels, data_height, data_width),
device='cpu')
detector_model = None
if args.detector == 'autoencoder1':
detector_model = autoencoder(n_channels)
elif args.detector == 'autoencoder2':
detector_model = autoencoder2(n_channels)
else:
print("wrong detector model : must be autoencoder1 or autoencoder2")
raise SystemExit
print('detector model')
summary(detector_model,
input_size=(n_channels, data_height, data_width),
device='cpu')
# set device configuration
device_ids = []
if gpu == 'gpu':
if not torch.cuda.is_available():
print("No cuda available")
raise SystemExit
device = torch.device(args.model_device1)
device_defense = torch.device(args.defense_model_device)
device_ids.append(args.model_device1)
if args.model_device2 != -1:
device_ids.append(args.model_device2)
if args.model_device3 != -1:
device_ids.append(args.model_device3)
if args.model_device4 != -1:
device_ids.append(args.model_device4)
else:
device = torch.device("cpu")
device_defense = torch.device("cpu")
detector = AEDetector(detector_model,
device_defense,
args.detector_path,
p=2)
reformer = SimpleReformer(reformer_model, device_defense,
args.reformer_path)
classifier = Classifier(model, device, args.model_path, device_ids)
# set testdataset
test_dataset = SampleDataset(data_path)
test_loader = DataLoader(
test_dataset,
batch_size=10,
num_workers=4,
)
print('test_dataset : {}, test_loader : {}'.format(len(test_dataset),
len(test_loader)))
# Defense with MagNet
print('test start')
for thrs in thresholds:
print('----------------------------------------')
counter = 0
avg_score = 0.0
thrs = torch.tensor(thrs)
with torch.no_grad():
for batch_idx, (inputs, labels) in enumerate(test_loader):
inputs = inputs.float()
labels = labels.to(device).long()
target = make_one_hot(labels[:, 0, :, :], n_classes, device)
operate_results = operate(reformer, classifier, inputs)
all_pass, _ = filters(detector, inputs, thrs)
if len(all_pass) == 0:
continue
filtered_results = operate_results[all_pass]
pred = filtered_results.to(device).float()
target = target[all_pass]
loss = dice_score(pred, target)
avg_score += loss.data.cpu().numpy()
# statistics
counter += 1
del inputs, labels, pred, target, loss
if counter:
avg_score = avg_score / counter
print('threshold : {:.4f}, avg_score : {:.4f}'.format(
thrs, avg_score))
else:
print(
'threshold : {:.4f} , no images pass from filter'.format(thrs))