def visualize(args, path):
model = ProbabilisticUnet(input_channels=1, num_classes=1, num_filters=[32,64,128,192], latent_dim=2, no_convs_fcomb=4, beta=10.0)
model.to(device)
model.load_state_dict(torch.load(path))
task_dir = args.task
testset = MedicalDataset(task_dir=task_dir, mode='test')
testloader = data.DataLoader(testset, batch_size=1, shuffle=False)
model.eval()
with torch.no_grad():
while testset.iteration < args.test_iteration:
x, y = testset.next()
x, y = torch.from_numpy(x).unsqueeze(0).cuda(), torch.from_numpy(y).unsqueeze(0).cuda()
#output = torch.nn.Sigmoid()(model(x))
#output = torch.round(output)
output = model.forward(x,y,training=True)
output = torch.round(output)
#elbo = model.elbo(y)
# reg_loss = l2_regularisation(model.posterior) + l2_regularisation(model.prior) + l2_regularisation(model.fcomb.layers)
# valid_loss = -elbo + 1e-5 * reg_loss
print (x.size(), y.size(), output.size())
grid = torch.cat((x,y,output), dim=0)
torchvision.utils.save_image(grid, './save/'+testset.task_dir+'prediction'+str(testset.iteration)+'.png', nrow=8, padding=2, pad_value=1)
# model
net = ProbabilisticUnet(input_channels=1,
num_classes=1,
num_filters=[32, 64, 128, 192],
latent_dim=2,
no_convs_fcomb=4,
beta=10.0)
if LOAD_MODEL_FROM is not None:
import os
net.load_state_dict(
torch.load(os.path.join("./saved_checkpoints/", LOAD_MODEL_FROM)))
net.to(device)
net.eval()
def energy_distance(seg_samples, gt_seg_modes, num_samples=2):
num_modes = 4 # fixed for LIDC
# if num_samples != len(seg_samples) or num_samples != len(gt_seg_modes):
# raise ValueError
d_matrix_YS = np.zeros(shape=(num_modes, num_samples), dtype=np.float32)
d_matrix_YY = np.zeros(shape=(num_modes, num_modes), dtype=np.float32)
d_matrix_SS = np.zeros(shape=(num_samples, num_samples), dtype=np.float32)
# iterate all ground-truth modes
for mode in range(num_modes):
def train(args):
num_epoch = args.epoch
learning_rate = args.learning_rate
task_dir = args.task
trainset = MedicalDataset(task_dir=task_dir, mode='train' )
validset = MedicalDataset(task_dir=task_dir, mode='valid')
model = ProbabilisticUnet(input_channels=1, num_classes=1, num_filters=[32,64,128,192], latent_dim=2, no_convs_fcomb=4, beta=10.0)
model.to(device)
#summary(model, (1,320,320))
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0)
criterion = torch.nn.BCELoss()
for epoch in range(num_epoch):
model.train()
while trainset.iteration < args.iteration:
x, y = trainset.next()
x, y = torch.from_numpy(x).unsqueeze(0).cuda(), torch.from_numpy(y).unsqueeze(0).cuda()
#print(x.size(), y.size())
#output = torch.nn.Sigmoid()(model(x))
model.forward(x,y,training=True)
elbo = model.elbo(y)
reg_loss = l2_regularisation(model.posterior) + l2_regularisation(model.prior) + l2_regularisation(model.fcomb.layers)
loss = -elbo + 1e-5 * reg_loss
#loss = criterion(output, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
trainset.iteration = 0
model.eval()
with torch.no_grad():
while validset.iteration < args.test_iteration:
x, y = validset.next()
x, y = torch.from_numpy(x).unsqueeze(0).cuda(), torch.from_numpy(y).unsqueeze(0).cuda()
#output = torch.nn.Sigmoid()(model(x, y))
model.forward(x,y,training=True)
elbo = model.elbo(y)
reg_loss = l2_regularisation(model.posterior) + l2_regularisation(model.prior) + l2_regularisation(model.fcomb.layers)
valid_loss = -elbo + 1e-5 * reg_loss
validset.iteration = 0
print('Epoch: {}, elbo: {:.4f}, regloss: {:.4f}, loss: {:.4f}, valid loss: {:.4f}'.format(epoch+1, elbo.item(), reg_loss.item(), loss.item(), valid_loss.item()))
"""
#Logger
# 1. Log scalar values (scalar summary)
info = { 'loss': loss.item(), 'accuracy': valid_loss.item() }
for tag, value in info.items():
Logger.scalar_summary(tag, value, epoch+1)
# 2. Log values and gradients of the parameters (histogram summary)
for tag, value in model.named_parameters():
tag = tag.replace('.', '/')
Logger.histo_summary(tag, value.data.cpu().numpy(), epoch+1)
Logger.histo_summary(tag+'/grad', value.grad.data.cpu().numpy(), epoch+1)
"""
torch.save(model.state_dict(), './save/'+trainset.task_dir+'model.pth')