def test_medical(device, model=None, test_organ='liver'):
assert cfg['panet']['backbone'] == 'unet'
dataset_name = 'ircadb'
if model is None:
model = PANetFewShotSeg(
in_channels=cfg[dataset_name]['channels'],
pretrained_path=None,
cfg={
'align': True
},
encoder_type=cfg['panet']['backbone']).to(device)
load_state(cfg[dataset_name]['model_name'], model)
model.eval()
transforms = Compose([
Resize(size=cfg['panet']['unet_inp_size']),
])
test_dataset = get_visceral_medical_few_shot_dataset(organs=[test_organ],
patient_ids=range(
16, 20),
shots=5,
mode='test',
transforms=transforms)
testloader = DataLoader(test_dataset,
batch_size=1,
shuffle=True,
num_workers=1,
pin_memory=True,
drop_last=True)
metric = Metric(max_label=20, n_runs=1)
for i_iter, (support,
query) in enumerate(tqdm(testloader, position=0, leave=True)):
support_images = [[]]
support_fg_mask = [[]]
support_bg_mask = [[]]
# for i in range(len(support)):
support_bg = support[1] == 0
support_fg = support[1] == 1
support_images[0].append(support[0].to(device))
support_fg_mask[0].append(support_fg.float().to(device))
support_bg_mask[0].append(support_bg.float().to(device))
query_images = []
query_labels = []
# for i in range(len(query)):
query_images.append(query[0].to(device))
query_labels.append(query[1].to(device))
query_labels = torch.cat(query_labels, dim=0).long().to(device)
query_pred, _ = model(support_images, support_fg_mask, support_bg_mask,
query_images)
print("Support ", i_iter)
plt.subplot(1, 2, 1)
try:
plt.imshow(
np.moveaxis(support[0].squeeze().cpu().detach().numpy(), 0, 2))
except np.AxisError:
plt.imshow(support[0].squeeze().cpu().detach().numpy())
plt.subplot(1, 2, 2)
plt.imshow(support[1].squeeze())
plt.show()
print("Query ", i_iter)
plt.subplot(1, 3, 1)
try:
plt.imshow(
np.moveaxis(query[0].squeeze().cpu().detach().numpy(), 0, 2))
except np.AxisError:
plt.imshow(query[0].squeeze().cpu().detach().numpy())
plt.subplot(1, 3, 2)
plt.imshow(query[1].squeeze())
plt.subplot(1, 3, 3)
plt.imshow(np.array(query_pred.argmax(dim=1)[0].cpu()))
metric.record(np.array(query_pred.argmax(dim=1)[0].cpu()),
np.array(query_labels[0].cpu()),
n_run=0)
plt.show()
classIoU, meanIoU = metric.get_mIoU(n_run=0)
classIoU_binary, meanIoU_binary = metric.get_mIoU_binary(n_run=0)
print('classIoU', classIoU.tolist())
print('meanIoU', meanIoU.tolist())
print('classIoU_binary', classIoU_binary.tolist())
print('meanIoU_binary', meanIoU_binary.tolist())
print('classIoU: {}'.format(classIoU))
print('meanIoU: {}'.format(meanIoU))
print('classIoU_binary: {}'.format(classIoU_binary))
print('meanIoU_binary: {}'.format(meanIoU_binary))
def test_panet(device, model=None, dataset_name='voc', test_organ='liver'):
if model is None:
# pretrained_path='../data/vgg16-397923af.pth'
model = PANetFewShotSeg(
in_channels=cfg[dataset_name]['channels'],
pretrained_path=None,
cfg={
'align': True
},
encoder_type=cfg['panet']['backbone']).to(device)
load_state(cfg[dataset_name]['model_name'], model)
model.eval()
if dataset_name == 'voc':
transforms = Compose([
Resize(size=cfg['panet']['vgg_inp_size']),
])
elif dataset_name == 'ircadb':
transforms = Compose([
Resize(size=cfg['panet']['unet_inp_size']),
])
if dataset_name == 'voc':
test_dataset = get_pascal_few_shot_datasets(
range(16, 21), cfg['panet']['test_iterations'], cfg['nshot'],
cfg['nquery'], transforms)
elif dataset_name == 'ircadb':
test_dataset = get_ircadb_few_shot_datasets(
organs=[test_organ],
patient_ids=range(16, 21),
iterations=cfg['panet']['test_iterations'],
N_shot=cfg['nshot'],
N_query=cfg['nquery'],
transforms=transforms)
testloader = DataLoader(test_dataset,
batch_size=1,
shuffle=True,
num_workers=1,
pin_memory=True,
drop_last=True)
metric = Metric(max_label=20, n_runs=1)
for i_iter, (support, query) in enumerate(testloader):
support_images = [[]]
support_fg_mask = [[]]
support_bg_mask = [[]]
for i in range(len(support)):
support_images[0].append(support[i][0].to(device))
support_fg_mask[0].append(support[i][1].to(device))
support_bg_mask[0].append(support[i][2].to(device))
query_images = []
query_labels = []
for i in range(len(query)):
query_images.append(query[i][0].to(device))
query_labels.append(query[i][1].to(device))
query_labels = torch.cat(query_labels, dim=0).long().to(device)
query_pred, _ = model(support_images, support_fg_mask, support_bg_mask,
query_images)
print("Support ", i_iter)
for i in range(len(support)):
plt.subplot(1, 2 * len(support), 2 * i + 1)
try:
plt.imshow(
np.moveaxis(support[i][0].squeeze().cpu().detach().numpy(),
0, 2))
except np.AxisError:
plt.imshow(support[i][0].squeeze().cpu().detach().numpy())
plt.subplot(1, 2 * len(support), 2 * i + 2)
plt.imshow(support[i][1].squeeze())
plt.show()
print("Query ", i_iter)
for i in range(len(query)):
plt.subplot(1, 3 * len(query), 3 * i + 1)
try:
plt.imshow(
np.moveaxis(query[i][0].squeeze().cpu().detach().numpy(),
0, 2))
except np.AxisError:
plt.imshow(query[i][0].squeeze().cpu().detach().numpy())
plt.subplot(1, 3 * len(query), 3 * i + 2)
plt.imshow(query[i][1].squeeze())
plt.subplot(1, 3 * len(query), 3 * i + 3)
plt.imshow(np.array(query_pred.argmax(dim=1)[i].cpu()))
metric.record(np.array(query_pred.argmax(dim=1)[i].cpu()),
np.array(query_labels[i].cpu()),
n_run=0)
plt.show()
classIoU, meanIoU = metric.get_mIoU(n_run=0)
classIoU_binary, meanIoU_binary = metric.get_mIoU_binary(n_run=0)
print('classIoU', classIoU.tolist())
print('meanIoU', meanIoU.tolist())
print('classIoU_binary', classIoU_binary.tolist())
print('meanIoU_binary', meanIoU_binary.tolist())
print('classIoU: {}'.format(classIoU))
print('meanIoU: {}'.format(meanIoU))
print('classIoU_binary: {}'.format(classIoU_binary))
print('meanIoU_binary: {}'.format(meanIoU_binary))