def train_lvl1(): print("Training lvl1...") device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') model = Miccai2020_LDR_laplacian_unit_disp_add_lvl1( 2, 3, start_channel, is_train=True, imgshape=imgshape_4, range_flow=range_flow).to(device) loss_similarity = NCC(win=3) loss_Jdet = neg_Jdet_loss loss_smooth = smoothloss transform = SpatialTransform_unit().to(device) for param in transform.parameters(): param.requires_grad = False param.volatile = True # OASIS names = sorted(glob.glob(datapath + '/*.nii')) grid_4 = generate_grid(imgshape_4) grid_4 = torch.from_numpy(np.reshape(grid_4, (1, ) + grid_4.shape)).to(device).float() optimizer = torch.optim.Adam(model.parameters(), lr=lr) # optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9) model_dir = '../Model/Stage' if not os.path.isdir(model_dir): os.mkdir(model_dir) lossall = np.zeros((4, iteration_lvl1 + 1)) training_generator = Data.DataLoader(Dataset_epoch(names, norm=False), batch_size=1, shuffle=True, num_workers=2) step = 0 load_model = False if load_model is True: model_path = "../Model/LDR_LPBA_NCC_lap_share_preact_1_05_3000.pth" print("Loading weight: ", model_path) step = 3000 model.load_state_dict(torch.load(model_path)) temp_lossall = np.load( "../Model/loss_LDR_LPBA_NCC_lap_share_preact_1_05_3000.npy") lossall[:, 0:3000] = temp_lossall[:, 0:3000] while step <= iteration_lvl1: for X, Y in training_generator: X = X.to(device).float() Y = Y.to(device).float() # output_disp_e0, warpped_inputx_lvl1_out, down_y, output_disp_e0_v, e0 F_X_Y, X_Y, Y_4x, F_xy, _ = model(X, Y) # 3 level deep supervision NCC loss_multiNCC = loss_similarity(X_Y, Y_4x) F_X_Y_norm = transform_unit_flow_to_flow_cuda( F_X_Y.permute(0, 2, 3, 4, 1).clone()) loss_Jacobian = loss_Jdet(F_X_Y_norm, grid_4) # reg2 - use velocity _, _, x, y, z = F_X_Y.shape F_X_Y[:, 0, :, :, :] = F_X_Y[:, 0, :, :, :] * z F_X_Y[:, 1, :, :, :] = F_X_Y[:, 1, :, :, :] * y F_X_Y[:, 2, :, :, :] = F_X_Y[:, 2, :, :, :] * x loss_regulation = loss_smooth(F_X_Y) loss = loss_multiNCC + antifold * loss_Jacobian + smooth * loss_regulation optimizer.zero_grad() # clear gradients for this training step loss.backward() # backpropagation, compute gradients optimizer.step() # apply gradients lossall[:, step] = np.array([ loss.item(), loss_multiNCC.item(), loss_Jacobian.item(), loss_regulation.item() ]) sys.stdout.write( "\r" + 'step "{0}" -> training loss "{1:.4f}" - sim_NCC "{2:4f}" - Jdet "{3:.10f}" -smo "{4:.4f}"' .format(step, loss.item(), loss_multiNCC.item(), loss_Jacobian.item(), loss_regulation.item())) sys.stdout.flush() # with lr 1e-3 + with bias if (step % n_checkpoint == 0): modelname = model_dir + '/' + model_name + "stagelvl1_" + str( step) + '.pth' torch.save(model.state_dict(), modelname) np.save( model_dir + '/loss' + model_name + "stagelvl1_" + str(step) + '.npy', lossall) step += 1 if step > iteration_lvl1: break print("one epoch pass") np.save(model_dir + '/loss' + model_name + 'stagelvl1.npy', lossall)
def train(lvlID, opt=[], model_lvl1_path="", model_lvl2_path=""): print("Training " + str(lvlID) + "===========================================================") model_dir = '../Model/Stage' if not os.path.isdir(model_dir): os.mkdir(model_dir) result_folder_path = "../Results" logLvlPath = model_dir + "/logLvl_" + str(lvlID) + ".txt" #logLvlChrtPath = model_dir + "/logLvl_"+str(lvlID)+".png" numWorkers = 0 # 2 number of threads for the data generators??? #device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') freeze_step = opt.freeze_step # TODO: ??? lossName = "_NCC_" if opt.simLossType == 0 else ( "_MSE_" if opt.simLossType == 1 else "_DICE_") model_name = "LDR_OASIS" + lossName + "_disp_" + str( opt.start_channel) + "_" + str(opt.iteration_lvl1) + "_f" + str( opt.iteration_lvl2) + "_f" + str(opt.iteration_lvl3) + "_" device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') model_lvl_path = model_dir + '/' + model_name + "stagelvl" + str( lvlID) + "_0.pth" loss_lvl_path = model_dir + '/loss' + model_name + "stagelvl" + str( lvlID) + "_0.npy" n_checkpoint = opt.checkpoint if lvlID == 1: model = Miccai2020_LDR_laplacian_unit_disp_add_lvl1( in_channel, n_classes, start_channel, is_train=isTrainLvl1, imgshape=imgshape_4, range_flow=range_flow).to(device) grid = generate_grid(imgshape_4) start_iteration = opt.sIteration_lvl1 num_iteration = opt.iteration_lvl1 elif lvlID == 2: model_lvl1 = Miccai2020_LDR_laplacian_unit_disp_add_lvl1( in_channel, n_classes, start_channel, is_train=isTrainLvl1, imgshape=imgshape_4, range_flow=range_flow).to(device) model_lvl1.load_state_dict(torch.load(model_lvl1_path)) # Freeze model_lvl1 weight for param in model_lvl1.parameters(): param.requires_grad = False model = Miccai2020_LDR_laplacian_unit_disp_add_lvl2( in_channel, n_classes, start_channel, is_train=isTrainLvl2, imgshape=imgshape_2, range_flow=range_flow, model_lvl1=model_lvl1).to(device) grid = generate_grid(imgshape_2) start_iteration = opt.sIteration_lvl2 num_iteration = opt.iteration_lvl2 elif lvlID == 3: model_lvl1 = Miccai2020_LDR_laplacian_unit_disp_add_lvl1( in_channel, n_classes, start_channel, is_train=isTrainLvl1, imgshape=imgshape_4, range_flow=range_flow).to(device) model_lvl2 = Miccai2020_LDR_laplacian_unit_disp_add_lvl2( in_channel, n_classes, start_channel, is_train=isTrainLvl2, imgshape=imgshape_2, range_flow=range_flow, model_lvl1=model_lvl1).to(device) model_lvl2.load_state_dict(torch.load(model_lvl2_path)) # Freeze model_lvl1 weight for param in model_lvl2.parameters(): param.requires_grad = False model = Miccai2020_LDR_laplacian_unit_disp_add_lvl3( in_channel, n_classes, start_channel, is_train=isTrainLvl3, imgshape=imgshape, range_flow=range_flow, model_lvl2=model_lvl2).to(device) grid = generate_grid(imgshape) start_iteration = opt.sIteration_lvl3 num_iteration = opt.iteration_lvl3 load_model_lvl = True if start_iteration > 0 else False loss_Jdet = neg_Jdet_loss loss_smooth = smoothloss transform = SpatialTransform_unit().to(device) for param in transform.parameters(): param.requires_grad = False # TODO: ??? param.volatile = True # TODO: ??? grid = torch.from_numpy(np.reshape(grid, (1, ) + grid.shape)).to(device).float() optimizer = torch.optim.Adam(model.parameters(), lr=lr) # optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9) lossall = np.zeros((4, num_iteration + 1)) #TODO: improve the data generator: # - use fixed lists for training and testing # - use augmentation # names, norm=1, aug=1, isSeg=0 , new_size=[0,0,0]): training_generator = Data.DataLoader(Dataset_epoch(trainingLst, norm=doNormalisation, aug=opt.doAugmentation, isSeg=opt.isSeg), batch_size=1, shuffle=True, num_workers=numWorkers) step = 0 if start_iteration > 0: model_lvl_path = model_dir + '/' + model_name + "stagelvl" + str( lvlID) + "_" + str(num_iteration) + '.pth' loss_lvl_path = model_dir + '/loss' + model_name + "stagelvl" + str( lvlID) + "_" + str(num_iteration) + '.npy' print("Loading weight and loss : ", model_lvl_path) step = num_iteration + 1 model.load_state_dict(torch.load(model_lvl_path)) temp_lossall = np.load(loss_lvl_path) lossall[:, 0:num_iteration] = temp_lossall[:, 0:num_iteration] else: #create log file only when logLvlFile = open(logLvlPath, "w") logLvlFile.close stepsLst = [] lossLst = [] simNCCLst = [] JdetLst = [] smoLst = [] # for each iteration #TODO: modify the iteration to be related to the number of images while step <= num_iteration: #for each pair in the data generator for pair in training_generator: X = pair[0][0] Y = pair[0][1] movingPath = pair[1][0][0] fixedPath = pair[1][1][0] ext = ".nii.gz" if ".nii.gz" in fixedPath else ( ".nii" if ".nii" in fixedPath else ".nrrd") X = X.to(device).float() Y = Y.to(device).float() assert not np.any(np.isnan(X.cpu().numpy())) assert not np.any(np.isnan(Y.cpu().numpy())) # output_disp_e0, warpped_inputx_lvl1_out, down_y, output_disp_e0_v, e0 # F_X_Y: displacement_field, # X_Y: wrapped_moving_image, # Y_4x: downsampled_fixed_image, # F_xy: velocity_field if lvlID == 1: F_X_Y, X_Y, Y_4x, F_xy, _ = model(X, Y) elif lvlID == 2: F_X_Y, X_Y, Y_4x, F_xy, F_xy_lvl1, _ = model(X, Y) elif lvlID == 3: F_X_Y, X_Y, Y_4x, F_xy, F_xy_lvl1, F_xy_lvl2, _ = model(X, Y) # print("Y_4x shape : ",Y_4x.shape) # print("X_Y shape : ",X_Y.shape) if opt.simLossType == 0: # NCC if lvlID == 1: loss_similarity = NCC(win=3) elif lvlID == 2: loss_similarity = multi_resolution_NCC(win=5, scale=2) elif lvlID == 3: loss_similarity = multi_resolution_NCC(win=7, scale=3) loss_sim = loss_similarity(X_Y, Y_4x) elif opt.simLossType == 1: # mse loss #loss_sim = mseLoss(X_Y, Y_4x) loss_sim = mse_loss(X_Y, Y_4x) #print("loss_sim : ",loss_sim) elif opt.simLossType == 2: # Dice loss # transform seg dv = math.pow(2, 3 - lvlID) fixedSeg = img2SegTensor(fixedPath, ext, dv) movingSeg = img2SegTensor(movingPath, ext, dv) movingSeg = movingSeg[np.newaxis, ...] movingSeg = torch.from_numpy(movingSeg).float().to( device).unsqueeze(dim=0) transformedSeg = transform(movingSeg, F_X_Y.permute(0, 2, 3, 4, 1), grid).data.cpu().numpy()[0, 0, :, :, :] transformedSeg[transformedSeg > 0] = 1.0 loss_sim = diceLoss(fixedSeg, transformedSeg) loss_sim = DiceLoss.getDiceLoss(fixedSeg, transformedSeg) else: print("error: not supported loss ........") # 3 level deep supervision NCC F_X_Y_norm = transform_unit_flow_to_flow_cuda( F_X_Y.permute(0, 2, 3, 4, 1).clone()) loss_Jacobian = loss_Jdet(F_X_Y_norm, grid) # reg2 - use velocity _, _, x, y, z = F_X_Y.shape F_X_Y[:, 0, :, :, :] = F_X_Y[:, 0, :, :, :] * z F_X_Y[:, 1, :, :, :] = F_X_Y[:, 1, :, :, :] * y F_X_Y[:, 2, :, :, :] = F_X_Y[:, 2, :, :, :] * x loss_regulation = loss_smooth(F_X_Y) assert not np.any(np.isnan(loss_sim.item())) assert not np.any(np.isnan(loss_Jacobian.item())) assert not np.any(np.isnan(loss_regulation.item())) loss = loss_sim + antifold * loss_Jacobian + smooth * loss_regulation assert not np.any(np.isnan(loss.item())) # TODO: ??? why clearing optimiser evey new example? optimizer.zero_grad() # clear gradients for this training step loss.backward() # backpropagation, compute gradients optimizer.step() # apply gradients lossall[:, step] = np.array([ loss.item(), loss_sim.item(), loss_Jacobian.item(), loss_regulation.item() ]) logLine = "\r" + 'step "{0}" -> training loss "{1:.4f}" - sim "{2:4f}" - Jdet "{3:.10f}" -smo "{4:.4f}"'.format( step, loss.item(), loss_sim.item(), loss_Jacobian.item(), loss_regulation.item()) #sys.stdout.write(logLine) #sys.stdout.flush() print(logLine) logLvlFile = open(logLvlPath, "a") logLvlFile.write(logLine) logLvlFile.close() # with lr 1e-3 + with bias if lvlID == 3: n_checkpoint = 10 if (step % n_checkpoint == 0): model_lvl_path = model_dir + '/' + model_name + "stagelvl" + str( lvlID) + "_" + str(step) + '.pth' loss_lvl_path = model_dir + '/loss' + model_name + "stagelvl" + str( lvlID) + "_" + str(step) + '.npy' torch.save(model.state_dict(), model_lvl_path) # np.save(loss_lvl_path, lossall) iaLog2Fig(logLvlPath) if doValidation: iaVal = validate(model) if (lvlID == 3) and (step == freeze_step): model.unfreeze_modellvl2() step += 1 if step > num_iteration: break print("one epoch pass ....") model_lvl_path = model_dir + '/' + model_name + "stagelvl" + str( lvlID) + "_" + str(num_iteration) + '.pth' loss_lvl_path = model_dir + '/' + 'loss' + model_name + "stagelvl" + str( lvlID) + "_" + str(num_iteration) + '.npy' torch.save(model.state_dict(), model_lvl_path) #np.save(loss_lvl_path, lossall) return model_lvl_path