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)
print("Number of test patches:", (len(eval_indices))) # 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')