def train(): # Init data train_dataset, val_dataset = prepare_datasets() train_loader = DataLoader(train_dataset, batch_size=10, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=10, shuffle=True) loaders = dict(train=train_loader, val=val_loader) # Init Model model = UNet().cuda() optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, amsgrad=True) scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=optimizer, gamma=0.984) loss_fn = nn.BCELoss() epochs = 500 for epoch in range(epochs): for phase in 'train val'.split(): if phase == 'train': model = model.train() torch.set_grad_enabled(True) else: model = model.eval() torch.set_grad_enabled(False) loader = loaders[phase] epoch_losses = dict(train=[], val=[]) running_loss = [] for batch in loader: imgs, masks = batch imgs = imgs.cuda() masks = masks.cuda() outputs = model(imgs) loss = loss_fn(outputs, masks) running_loss.append(loss.item()) if phase == 'train': optimizer.zero_grad() loss.backward() optimizer.step() # End of Epoch print(f'{epoch}) {phase} loss: {np.mean(running_loss)}') visualize_results(loader, model, epoch, phase) epoch_losses[phase].append(np.mean(running_loss)) tensorboard(epoch_losses[phase], phase) if phase == 'train': scheduler.step()
partition = 'train' unet_train = HistologyData(ROOT_DIR, partition, True) unet_loader = torch.utils.data.DataLoader( unet_train, batch_size=1, shuffle=True, ) # Create model model = ShapeUNet((15, 512, 512)) unet = UNet((3, 512, 512)) model.to(device) unet.to(device) mask_values = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14) # here not RGB but BGR because of OPENCV. real_colors = ((0, 0, 0), (255, 0, 0), (0, 255, 0), (0, 0, 255), (85, 0, 0), (0, 170, 0), (255, 0, 127), (0, 255, 255), (0, 85, 0), (255, 0, 255), (255, 85, 0), (255, 165, 0), (255, 255, 0), (128, 130, 128), (128, 190, 190)) lr = 1e-4 optimizer = Adam(model.parameters(), lr=lr) NUM_OF_EPOCHS = 40 lr1 = 1e-4 unet_optim = Adam(unet.parameters(), lr=lr1) train_network_on_top_of_other(model, train_loader, val_loader, optimizer, unet, unet_loader, unet_optim, NUM_OF_EPOCHS, 'weights/')
def train(frame_num, layer_nums, input_channels, output_channels, discriminator_num_filters, bn=False, pretrain=False, generator_pretrain_path=None, discriminator_pretrain_path=None): generator = UNet(n_channels=input_channels, layer_nums=layer_nums, output_channel=output_channels, bn=bn) discriminator = PixelDiscriminator(output_channels, discriminator_num_filters, use_norm=False) generator = generator.cuda() discriminator = discriminator.cuda() flow_network = Network() flow_network.load_state_dict(torch.load(lite_flow_model_path)) flow_network.cuda().eval() adversarial_loss = Adversarial_Loss().cuda() discriminate_loss = Discriminate_Loss().cuda() gd_loss = Gradient_Loss(alpha, num_channels).cuda() op_loss = Flow_Loss().cuda() int_loss = Intensity_Loss(l_num).cuda() step = 0 if not pretrain: generator.apply(weights_init_normal) discriminator.apply(weights_init_normal) else: assert (generator_pretrain_path != None and discriminator_pretrain_path != None) generator.load_state_dict(torch.load(generator_pretrain_path)) discriminator.load_state_dict(torch.load(discriminator_pretrain_path)) step = int(generator_pretrain_path.split('-')[-1]) print('pretrained model loaded!') print('initializing the model with Generator-Unet {} layers,' 'PixelDiscriminator with filters {} '.format( layer_nums, discriminator_num_filters)) optimizer_G = torch.optim.Adam(generator.parameters(), lr=g_lr) optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=d_lr) writer = SummaryWriter(writer_path) dataset = img_dataset.ano_pred_Dataset(training_data_folder, frame_num) dataset_loader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True, num_workers=1, drop_last=True) test_dataset = img_dataset.ano_pred_Dataset(testing_data_folder, frame_num) test_dataloader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True, num_workers=1, drop_last=True) for epoch in range(epochs): for (input, _), (test_input, _) in zip(dataset_loader, test_dataloader): # generator = generator.train() # discriminator = discriminator.train() target = input[:, -1, :, :, :].cuda() input = input[:, :-1, ] input_last = input[:, -1, ].cuda() input = input.view(input.shape[0], -1, input.shape[-2], input.shape[-1]).cuda() test_target = test_input[:, -1, ].cuda() test_input = test_input[:, :-1].view(test_input.shape[0], -1, test_input.shape[-2], test_input.shape[-1]).cuda() #------- update optim_G -------------- G_output = generator(input) pred_flow_esti_tensor = torch.cat([input_last, G_output], 1) gt_flow_esti_tensor = torch.cat([input_last, target], 1) flow_gt = batch_estimate(gt_flow_esti_tensor, flow_network) flow_pred = batch_estimate(pred_flow_esti_tensor, flow_network) g_adv_loss = adversarial_loss(discriminator(G_output)) g_op_loss = op_loss(flow_pred, flow_gt) g_int_loss = int_loss(G_output, target) g_gd_loss = gd_loss(G_output, target) g_loss = lam_adv * g_adv_loss + lam_gd * g_gd_loss + lam_op * g_op_loss + lam_int * g_int_loss optimizer_G.zero_grad() g_loss.backward() optimizer_G.step() train_psnr = psnr_error(G_output, target) #----------- update optim_D ------- optimizer_D.zero_grad() d_loss = discriminate_loss(discriminator(target), discriminator(G_output.detach())) #d_loss.requires_grad=True d_loss.backward() optimizer_D.step() #----------- cal psnr -------------- test_generator = generator.eval() test_output = test_generator(test_input) test_psnr = psnr_error(test_output, test_target).cuda() if step % 10 == 0: print("[{}/{}]: g_loss: {} d_loss {}".format( step, epoch, g_loss, d_loss)) print('\t gd_loss {}, op_loss {}, int_loss {} ,'.format( g_gd_loss, g_op_loss, g_int_loss)) print('\t train psnr{},test_psnr {}'.format( train_psnr, test_psnr)) writer.add_scalar('psnr/train_psnr', train_psnr, global_step=step) writer.add_scalar('psnr/test_psnr', test_psnr, global_step=step) writer.add_scalar('total_loss/g_loss', g_loss, global_step=step) writer.add_scalar('total_loss/d_loss', d_loss, global_step=step) writer.add_scalar('g_loss/adv_loss', g_adv_loss, global_step=step) writer.add_scalar('g_loss/op_loss', g_op_loss, global_step=step) writer.add_scalar('g_loss/int_loss', g_int_loss, global_step=step) writer.add_scalar('g_loss/gd_loss', g_gd_loss, global_step=step) writer.add_image('image/train_target', target[0], global_step=step) writer.add_image('image/train_output', G_output[0], global_step=step) writer.add_image('image/test_target', test_target[0], global_step=step) writer.add_image('image/test_output', test_output[0], global_step=step) step += 1 if step % 500 == 0: utils.saver(generator.state_dict(), model_generator_save_path, step, max_to_save=10) utils.saver(discriminator.state_dict(), model_discriminator_save_path, step, max_to_save=10) if step >= 2000: print('==== begin evaluate the model of {} ===='.format( model_generator_save_path + '-' + str(step))) auc = evaluate(frame_num=5, layer_nums=4, input_channels=12, output_channels=3, model_path=model_generator_save_path + '-' + str(step), evaluate_name='compute_auc') writer.add_scalar('results/auc', auc, global_step=step)
normalize=False) tf_predict = T.ToTensor() print(args.results_folder) print(args.train) print(args.val) print(args.test) if args.model_path is not None: net = torch.load(args.model_path) else: net = UNet(3, 1) loss = JaccardLoss() lr_milestones = [int(p * args.epochs) for p in [0.5, 0.7, 0.9]] optimizer = optim.Adam(net.parameters(), lr=1e-3) scheduler = optim.lr_scheduler.MultiStepLR(optimizer, lr_milestones) train_dataset = JointlyTransformedDataset(args.train, transform=tf_train, sigma=args.sigma) val_dataset = JointlyTransformedDataset(args.val, transform=tf_train, sigma=args.sigma) stage1_test_dataset = TestDataset(args.test, transform=tf_predict) model = Model( model=net, loss=loss, optimizer=optimizer, scheduler=scheduler,
input_ = torch.randn((bs, 3, height, width)).cuda() schedule = Schedule(graph, solver_info) schedule.init_schedule(solution, mode) torch.cuda.synchronize() start_event_monet = torch.cuda.Event(enable_timing=True) end_event_monet = torch.cuda.Event(enable_timing=True) for iterid in range(120): if iterid == 100: torch.cuda.reset_max_memory_allocated() start_event_monet.record() x1 = schedule.forward( input_, *list(model.state_dict(keep_vars=True).values())) schedule.backward(-torch.ones_like(x1)) for v in model.parameters(): v.grad = None end_event_monet.record() torch.cuda.synchronize() del x1 monet_maxmem = torch.cuda.max_memory_allocated() / 2**20 print("monet: %f ms avg, %8.2f MB" % (start_event_monet.elapsed_time(end_event_monet) / 20, monet_maxmem)) exit() if args.check_diff: graph = Graph.create(model, input_shape=(3, height, width)) model.cuda() input_ = torch.randn((bs, 3, height, width)).cuda()
from models.track_net import TrackNet from utils.get_dataloader import get_dataloader from env import post_slack from utils.detector import judge from models.unet import UNet def write_log(path, context, mode="a"): with open(path, mode=mode) as f: f.writelines(context + "\n") cuda0 = torch.device('cuda:0') net = UNet(27).to(cuda0) criterion = nn.MSELoss().to(cuda0) optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) train_data_laoder, test_data_loader = get_dataloader(batch_size=4) write_log("weight/train.log", str(datetime.datetime.now()), "w") write_log("weight/train.log", "train start") print(net) for epoch in range(300): # train phase running_loss = 0.0 net.train() for i, batch in enumerate(train_data_laoder): inputs = batch['image'].to(cuda0) target = batch['target'].to(cuda0) optimizer.zero_grad() outputs = net(inputs)
shuffle=False, num_workers=5, pin_memory=True) # net and optimizer ds_unet = UNet(1, 1, domain_specific=True) ds_unet.cuda() labeller = UNet(1, 1) # import weights here... labeller_path = './results/unet_sobel_eadan_in/net' labeller.load_state_dict( torch.load(os.path.join(labeller_path), map_location=lambda storage, loc: storage)) labeller.cuda() optimiser = optim.Adam(ds_unet.parameters(), lr=learning_rate) print('Project name ', project_name) train_dices = [] train_losses = [] val_a_dices = [] val_a_losses = [] val_b_dices = [] val_b_losses = [] for i in range(epochs): ds_unet.set_domain(DOMAIN_A) train_dice, train_loss = train_segmentation_net(ds_unet, train_a_loader,
(-x0).sum().backward() KEEP_FWDOP = True x1 = schedule.forward(input_, *list(model.state_dict(keep_vars=True).values())) print( 'Forward mean absolute difference', abs(x0[0] - x1).mean() if 'googlenet' in args.model else abs(x0 - x1).mean()) print('original output', x0) print('ours output', x1) print('Gradient of normal model', [ '{:.5f} {}'.format(float(v.grad.mean()), v.shape) for v in model.parameters() if v.grad is not None ]) if args.check_runtime: FORWARD_EMPTY_CACHE = False if len(args.solution_file) > 0: solver_info, solution = load_solution(args.solution_file) else: input_ = torch.randn((bs, 3, height, width)).cuda() if args.pipeline: solver_info = PipelinedSolverInfo(bs=bs, model_name=model_name, mode=mode) else: solver_info = SolverInfo(bs=bs,
def train_eval_model(opts): # parse model configuration num_epochs = opts["num_epochs"] train_batch_size = opts["train_batch_size"] val_batch_size = opts["eval_batch_size"] dataset_type = opts["dataset_type"] opti_mode = opts["optimizer"] loss_criterion = opts["loss_criterion"] lr = opts["lr"] lr_decay = opts["lr_decay"] wd = opts["weight_decay"] gpus = opts["gpu_list"].split(',') os.environ['CUDA_VISIBLE_DEVICE'] = opts["gpu_list"] train_dir = opts["log_dir"] train_data_dir = opts["train_data_dir"] eval_data_dir = opts["eval_data_dir"] pretrained = opts["pretrained_model"] resume = opts["resume"] display_iter = opts["display_iter"] save_epoch = opts["save_every_epoch"] show = opts["vis"] # backup train configs log_file = os.path.join(train_dir, "log_file.txt") os.makedirs(train_dir, exist_ok=True) model_dir = os.path.join(train_dir, "code_backup") os.makedirs(model_dir, exist_ok=True) if resume is None and os.path.exists(log_file): os.remove(log_file) shutil.copy("./models/unet.py", os.path.join(model_dir, "unet.py")) shutil.copy("./trainer_unet.py", os.path.join(model_dir, "trainer_unet.py")) shutil.copy("./datasets/dataset.py", os.path.join(model_dir, "dataset.py")) ckt_dir = os.path.join(train_dir, "checkpoints") os.makedirs(ckt_dir, exist_ok=True) # format printing configs print("*" * 50) table_key = [] table_value = [] n = 0 for key, value in opts.items(): table_key.append(key) table_value.append(str(value)) n += 1 print_table([table_key, ["="] * n, table_value]) # format gpu list gpu_list = [] for str_id in gpus: id = int(str_id) gpu_list.append(id) # dataloader print("==> Create dataloader") dataloaders_dict = { "train": er_data_loader(train_data_dir, train_batch_size, dataset_type, is_train=True), "eval": er_data_loader(eval_data_dir, val_batch_size, dataset_type, is_train=False) } # define parameters of two networks print("==> Create network") num_channels = 1 num_classes = 1 model = UNet(num_channels, num_classes) init_weights(model) # loss layer criterion = create_criterion(criterion=loss_criterion) best_acc = 0.0 start_epoch = 0 # load pretrained model if pretrained is not None and os.path.isfile(pretrained): print("==> Train from model '{}'".format(pretrained)) checkpoint_gan = torch.load(pretrained) model.load_state_dict(checkpoint_gan['model_state_dict']) print("==> Loaded checkpoint '{}')".format(pretrained)) for param in model.parameters(): param.requires_grad = False # resume training elif resume is not None and os.path.isfile(resume): print("==> Resume from checkpoint '{}'".format(resume)) checkpoint = torch.load(resume) start_epoch = checkpoint['epoch'] + 1 best_acc = checkpoint['best_acc'] model_dict = model.state_dict() pretrained_dict = { k: v for k, v in checkpoint['model_state_dict'].items() if k in model_dict and v.size() == model_dict[k].size() } model_dict.update(pretrained_dict) model.load_state_dict(pretrained_dict) print("==> Loaded checkpoint '{}' (epoch {})".format( resume, checkpoint['epoch'] + 1)) # train from scratch else: print("==> Train from initial or random state.") # define mutiple-gpu mode device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") model.cuda() model = nn.DataParallel(model) # print learnable parameters print("==> List learnable parameters") for name, param in model.named_parameters(): if param.requires_grad == True: print("\t{}, size {}".format(name, param.size())) params_to_update = [{'params': model.parameters()}] # define optimizer print("==> Create optimizer") optimizer = create_optimizer(params_to_update, opti_mode, lr=lr, momentum=0.9, wd=wd) if resume is not None and os.path.isfile(resume): optimizer.load_state_dict(checkpoint['optimizer']) # start training since = time.time() # Each epoch has a training and validation phase print("==> Start training") total_steps = 0 for epoch in range(start_epoch, num_epochs): print('-' * 50) print("==> Epoch {}/{}".format(epoch + 1, num_epochs)) total_steps = train_one_epoch(epoch, total_steps, dataloaders_dict['train'], model, device, criterion, optimizer, lr, lr_decay, display_iter, log_file, show) epoch_acc, epoch_iou, epoch_f1 = eval_one_epoch( epoch, dataloaders_dict['eval'], model, device, log_file) if best_acc < epoch_acc and epoch >= 5: best_acc = epoch_acc torch.save( { 'epoch': epoch, 'model_state_dict': model.module.state_dict(), 'optimizer': optimizer.state_dict(), 'best_acc': best_acc }, os.path.join(ckt_dir, "best.pth")) if (epoch + 1) % save_epoch == 0 and (epoch + 1) >= 20: torch.save( { 'epoch': epoch, 'model_state_dict': model.module.state_dict(), 'optimizer': optimizer.state_dict(), 'best_iou': epoch_iou }, os.path.join(ckt_dir, "checkpoints_" + str(epoch + 1) + ".pth")) time_elapsed = time.time() - since time_message = 'Training complete in {:.0f}m {:.0f}s'.format( time_elapsed // 60, time_elapsed % 60) print(time_message) with open(log_file, "a+") as fid: fid.write('%s\n' % time_message) print('==> Best val Acc: {:4f}'.format(best_acc))
def denoising(noise_im, clean_im, LR=1e-2, sigma=3, rho=1, eta=0.5, total_step=30, prob1_iter=500, noise_level=None, result_root=None, f=None): input_depth = 3 latent_dim = 3 en_net = UNet(input_depth, latent_dim).to(device) de_net = UNet(latent_dim, input_depth).to(device) parameters = [p for p in en_net.parameters()] + [p for p in de_net.parameters()] optimizer = torch.optim.Adam(parameters, lr=LR) l2_loss = torch.nn.MSELoss().cuda() i0 = np_to_torch(noise_im).to(device) noise_im_torch = np_to_torch(noise_im).to(device) i0_til_torch = np_to_torch(noise_im).to(device) Y = torch.zeros_like(noise_im_torch).to(device) diff_original_np = noise_im.astype(np.float32) - clean_im.astype(np.float32) diff_original_name = 'Original_dis.png' save_hist(diff_original_np, result_root+diff_original_name) best_psnr = 0 for i in range(total_step): ################################# sub-problem 1 ############################### for i_1 in range(prob1_iter): optimizer.zero_grad() mean = en_net(noise_im_torch) z = sample_z(mean) out = de_net(z) total_loss = 0.5 * l2_loss(out, noise_im_torch) total_loss += 0.5 * (1/sigma**2)*l2_loss(mean, i0) total_loss += (rho/2) * l2_loss(i0 + Y, i0_til_torch) total_loss.backward() optimizer.step() with torch.no_grad(): i0 = ((1/sigma**2)*mean.detach() + rho*(i0_til_torch - Y)) / ((1/sigma**2) + rho) with torch.no_grad(): ################################# sub-problem 2 ############################### i0_np = torch_to_np(i0) Y_np = torch_to_np(Y) sig = eval_sigma(i+1, noise_level) i0_til_np = bm3d.bm3d_rgb(i0_np.transpose(1, 2, 0) + Y_np.transpose(1, 2, 0), sig).transpose(2, 0, 1) i0_til_torch = np_to_torch(i0_til_np).to(device) ################################# sub-problem 3 ############################### Y = Y + eta * (i0 - i0_til_torch) ############################################################################### Y_name = 'Y_{:04d}'.format(i) + '.png' i0_name = 'i0_num_epoch_{:04d}'.format(i) + '.png' mean_name = 'Latent_im_num_epoch_{:04d}'.format(i) + '.png' out_name = 'res_of_dec_num_epoch_{:04d}'.format(i) + '.png' diff_name = 'Latent_dis_num_epoch_{:04d}'.format(i) + '.png' Y_np = torch_to_np(Y) Y_norm_np = np.sqrt((Y_np*Y_np).sum(0)) save_heatmap(Y_norm_np, result_root + Y_name) save_torch(mean, result_root + mean_name) save_torch(out, result_root + out_name) save_torch(i0, result_root + i0_name) mean_np = torch_to_np(mean) diff_np = mean_np - clean_im save_hist(diff_np, result_root + diff_name) i0_til_np = torch_to_np(i0_til_torch).clip(0, 255) psnr = compare_psnr(clean_im.transpose(1, 2, 0), i0_til_np.transpose(1, 2, 0), 255) ssim = compare_ssim(clean_im.transpose(1, 2, 0), i0_til_np.transpose(1, 2, 0), multichannel=True, data_range=255) i0_til_pil = np_to_pil(i0_til_np) i0_til_pil.save(os.path.join(result_root, '{}'.format(i) + '.png')) print('Iteration: {:02d}, VAE Loss: {:f}, PSNR: {:f}, SSIM: {:f}'.format(i, total_loss.item(), psnr, ssim), file=f, flush=True) if best_psnr < psnr: best_psnr = psnr best_ssim = ssim else: break return i0_til_np, best_psnr, best_ssim
test_dataset = TestFromFolder(os.path.join(all_datasets, 'stage1_test/loc.csv'), transform=T.ToTensor(), remove_alpha=True) """ ----------------- ----- Model ----- ----------------- """ generator = UNet(3, 1) discriminator = Discriminator(4, 1) generator.cuda() discriminator.cuda() # lr = 0.001 seems to work WITHOUT PRETRAINING g_optim = optim.Adam(generator.parameters(), lr=0.001) d_optim = optim.Adam(discriminator.parameters(), lr=0.001) #g_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(g_optim, factor=0.1, verbose=True, patience=5) #d_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(d_optim, factor=0.1, verbose=True, patience=5) gan = GAN( g=generator, d=discriminator, g_optim=g_optim, d_optim=d_optim, g_loss=nn.MSELoss().cuda(), d_loss=nn.MSELoss().cuda(), #g_scheduler=g_scheduler, d_scheduler=d_scheduler ) # pretraining generator
batch_size=1, shuffle=False, num_workers=5, pin_memory=True) refine_net = UNet(1, 1, norm_layer=nn.BatchNorm2d, affine=True, track_running_stats=True) refine_net.cuda() # list optimisers here... # single optimiser variant 1 optimiser_ref = optim.Adam(refine_net.parameters(), lr=learning_rate) print('Project name ', project_name) train_dices = [] train_losses = [] val_dices = [] val_losses = [] for i in range(epochs): train_dice, train_loss = train_segmentation_net(refine_net, train_loader, 'A', 3, 1, criterion_seg,
test_loader = DataLoader(scan_dataset_test, batch_size=1, shuffle=False, num_workers=5, pin_memory=True) pseudo = UNet(1, 1) net = UNet(1, 1, domain_specific=True) pseudo.cuda() net.cuda() # list optimisers here... # single optimiser variant 1 optimiser_ps = optim.Adam(pseudo.parameters(), lr=learning_rate) optimiser_net = optim.Adam(net.parameters(), lr=learning_rate) print('Project name ', project_name) print('Learning rate ', learning_rate) print('Epochs ', epochs) train_loss = [] train_loss_rec = [] train_loss_seg = [] train_loss_seg_a = [] train_loss_seg_b = [] train_dice = [] val_loss_a = []
def train_UNet(): cfg = UnetConfig() train_transform = transforms.Compose([ GrayscaleNormalization(mean=0.5, std=0.5), RandomRotation(), RandomFlip(), ToTensor(), ]) val_transform = transforms.Compose([ GrayscaleNormalization(mean=0.5, std=0.5), ToTensor(), ]) # Set Dataset train_dataset = Dataset(imgs_dir=TRAIN_IMGS_DIR, labels_dir=TRAIN_LABELS_DIR, transform=train_transform) train_loader = DataLoader(train_dataset, batch_size=cfg.BATCH_SIZE, shuffle=True, num_workers=0) val_dataset = Dataset(imgs_dir=VAL_IMGS_DIR, labels_dir=VAL_LABELS_DIR, transform=val_transform) val_loader = DataLoader(val_dataset, batch_size=cfg.BATCH_SIZE, shuffle=False, num_workers=0) train_data_num = len(train_dataset) val_data_num = len(val_dataset) train_batch_num = int(np.ceil(train_data_num / cfg.BATCH_SIZE)) # np.ceil val_batch_num = int(np.ceil(val_data_num / cfg.BATCH_SIZE)) # Network net = UNet().to(device) print(count_parameters(net)) # Loss Function loss_fn = nn.BCEWithLogitsLoss().to(device) # Optimizer optim = torch.optim.Adam(params=net.parameters(), lr=cfg.LEARNING_RATE) # Tensorboard # train_writer = SummaryWriter(log_dir=TRAIN_LOG_DIR) # val_writer = SummaryWriter(log_dir=VAL_LOG_DIR) # Training start_epoch = 0 # Load Checkpoint File if os.listdir(os.path.join(CKPT_DIR, 'unet')): net, optim, start_epoch = load_net(ckpt_dir=os.path.join( CKPT_DIR, 'unet'), net=net, optim=optim) else: print('* Training from scratch') num_epochs = cfg.NUM_EPOCHS for epoch in range(start_epoch + 1, num_epochs + 1): net.train() train_loss_arr = list() for batch_idx, data in enumerate(train_loader, 1): # Forward Propagation img = data['img'].to(device) label = data['label'].to(device) output = net(img) # Backward Propagation optim.zero_grad() loss = loss_fn(output, label) loss.backward() optim.step() # Calc Loss Function train_loss_arr.append(loss.item()) print_form = '[Train] | Epoch: {:0>4d} / {:0>4d} | Batch: {:0>4d} / {:0>4d} | Loss: {:.4f}' print( print_form.format(epoch, num_epochs, batch_idx, train_batch_num, train_loss_arr[-1])) train_loss_avg = np.mean(train_loss_arr) # train_writer.add_scalar(tag='loss', scalar_value=train_loss_avg, global_step=epoch) # Validation (No Back Propagation) with torch.no_grad(): net.eval() # Evaluation Mode val_loss_arr = list() for batch_idx, data in enumerate(val_loader, 1): # Forward Propagation img = data['img'].to(device) label = data['label'].to(device) output = net(img) # Calc Loss Function loss = loss_fn(output, label) val_loss_arr.append(loss.item()) print_form = '[Validation] | Epoch: {:0>4d} / {:0>4d} | Batch: {:0>4d} / {:0>4d} | Loss: {:.4f}' print( print_form.format(epoch, num_epochs, batch_idx, val_batch_num, val_loss_arr[-1])) val_loss_avg = np.mean(val_loss_arr) # val_writer.add_scalar(tag='loss', scalar_value=val_loss_avg, global_step=epoch) print_form = '[Epoch {:0>4d}] Training Avg Loss: {:.4f} | Validation Avg Loss: {:.4f}' print(print_form.format(epoch, train_loss_avg, val_loss_avg)) if epoch % 10 == 0: save_net(ckpt_dir=os.path.join(CKPT_DIR, 'unet'), net=net, optim=optim, epoch=epoch)
def test_UNet(): cfg = UnetConfig() transform = transforms.Compose([ GrayscaleNormalization(mean=0.5, std=0.5), ToTensor(), ]) RESULTS_DIR = os.path.join(ROOT_DIR, 'test_results/unet') if not os.path.exists(RESULTS_DIR): os.makedirs(RESULTS_DIR) label_save_path = os.path.join(RESULTS_DIR, 'label') output_save_path = os.path.join(RESULTS_DIR, 'output') if not os.path.exists(label_save_path): os.makedirs(label_save_path, exist_ok=True) if not os.path.exists(output_save_path): os.makedirs(output_save_path, exist_ok=True) test_dataset = Dataset(imgs_dir=TEST_IMGS_DIR, labels_dir=TEST_LABELS_DIR, transform=transform) test_loader = DataLoader(test_dataset, batch_size=cfg.BATCH_SIZE, shuffle=False, num_workers=0) test_data_num = len(test_dataset) test_batch_num = int(np.ceil(test_data_num / cfg.BATCH_SIZE)) # Network net = UNet().to(device) # Loss Function loss_fn = nn.BCEWithLogitsLoss().to(device) # Optimizer optim = torch.optim.Adam(params=net.parameters(), lr=cfg.LEARNING_RATE) start_epoch = 0 # Load Checkpoint File if os.listdir(CKPT_DIR): net, optim, _ = load_net(ckpt_dir=os.path.join(CKPT_DIR, 'unet'), net=net, optim=optim) # Evaluation with torch.no_grad(): net.eval() loss_arr = list() for batch_idx, data in enumerate(test_loader, 1): # Forward Propagation img = data['img'].to(device) label = data['label'].to(device) output = net(img) # Calc Loss Function loss = loss_fn(output, label) loss_arr.append(loss.item()) print_form = '[Test] | Batch: {:0>4d} / {:0>4d} | Loss: {:.4f}' print(print_form.format(batch_idx, test_batch_num, loss_arr[-1])) label = to_numpy(label) output = to_numpy(classify_class(output)) for j in range(label.shape[0]): crt_id = int(test_batch_num * (batch_idx - 1) + j) plt.imsave(os.path.join(label_save_path, f'{crt_id:04}.png'), label[j].squeeze(), cmap='gray') plt.imsave(os.path.join(output_save_path, f'{crt_id:04}.png'), output[j].squeeze(), cmap='gray') unet_acc(output_save_path, label_save_path)
param_noise = False else: assert False else: assert False net = net.type(dtype) net_input = get_noise(input_depth, INPUT, img_np.shape[1:]).type(dtype) # In[ ]: # Compute number of parameters s = sum(np.prod(list(p.size())) for p in net.parameters()) print ('Number of params: %d' % s) # Loss mse = torch.nn.MSELoss().type(dtype) img_var = np_to_torch(img_np).type(dtype) mask_var = np_to_torch(img_mask_np).type(dtype) # # Main loop # In[ ]: i = 0
class Noise2Noise(object): """Implementation of Noise2Noise from Lehtinen et al. (2018).""" def __init__(self, params, trainable): """Initializes model.""" self.p = params self.trainable = trainable self._compile() #初始化模型 def _compile(self): """ Compiles model (architecture, loss function, optimizers, etc.). 初始化 网络、损失函数、优化器等 """ print('Noise2Noise: Learning Image Restoration without Clean Data (Lethinen et al., 2018)') # Model (3x3=9 channels for Monte Carlo since it uses 3 HDR buffers) 已删除蒙特卡洛相关代码 if self.p.noise_type == 'mc': self.is_mc = True self.model = UNet(in_channels=9) else: self.is_mc = False self.model = UNet(in_channels=3) # Set optimizer and loss, if in training mode # 如果 为训练,则初始化优化器和损失 if self.trainable: self.optim = Adam(self.model.parameters(), lr=self.p.learning_rate, betas=self.p.adam[:2], eps=self.p.adam[2]) # Learning rate adjustment self.scheduler = lr_scheduler.ReduceLROnPlateau(self.optim, patience=self.p.nb_epochs/4, factor=0.5, verbose=True) # Loss function if self.p.loss == 'hdr': assert self.is_mc, 'Using HDR loss on non Monte Carlo images' self.loss = HDRLoss() elif self.p.loss == 'l2': self.loss = nn.MSELoss() else: self.loss = nn.L1Loss() # CUDA support self.use_cuda = torch.cuda.is_available() and self.p.cuda if self.use_cuda: self.model = self.model.cuda() if self.trainable: self.loss = self.loss.cuda() def _print_params(self): """Formats parameters to print when training.""" print('Training parameters: ') self.p.cuda = self.use_cuda param_dict = vars(self.p) pretty = lambda x: x.replace('_', ' ').capitalize() print('\n'.join(' {} = {}'.format(pretty(k), str(v)) for k, v in param_dict.items())) print() def save_model(self, epoch, stats, first=False): """Saves model to files; can be overwritten at every epoch to save disk space.""" # Create directory for model checkpoints, if nonexistent if first: if self.p.clean_targets: ckpt_dir_name = f'{datetime.now():{self.p.noise_type}-clean-%H%M}' else: ckpt_dir_name = f'{datetime.now():{self.p.noise_type}-%H%M}' if self.p.ckpt_overwrite: if self.p.clean_targets: ckpt_dir_name = f'{self.p.noise_type}-clean' else: ckpt_dir_name = self.p.noise_type self.ckpt_dir = os.path.join(self.p.ckpt_save_path, ckpt_dir_name) if not os.path.isdir(self.p.ckpt_save_path): os.mkdir(self.p.ckpt_save_path) if not os.path.isdir(self.ckpt_dir): os.mkdir(self.ckpt_dir) # Save checkpoint dictionary if self.p.ckpt_overwrite: fname_unet = '{}/n2n-{}.pt'.format(self.ckpt_dir, self.p.noise_type) else: valid_loss = stats['valid_loss'][epoch] fname_unet = '{}/n2n-epoch{}-{:>1.5f}.pt'.format(self.ckpt_dir, epoch + 1, valid_loss) print('Saving checkpoint to: {}\n'.format(fname_unet)) torch.save(self.model.state_dict(), fname_unet) # Save stats to JSON fname_dict = '{}/n2n-stats.json'.format(self.ckpt_dir) with open(fname_dict, 'w') as fp: json.dump(stats, fp, indent=2) def load_model(self, ckpt_fname): """Loads model from checkpoint file.""" print('Loading checkpoint from: {}'.format(ckpt_fname)) if self.use_cuda: self.model.load_state_dict(torch.load(ckpt_fname)) else: self.model.load_state_dict(torch.load(ckpt_fname, map_location='cpu')) def _on_epoch_end(self, stats, train_loss, epoch, epoch_start, valid_loader): """Tracks and saves starts after each epoch.""" # Evaluate model on validation set print('\rTesting model on validation set... ', end='') epoch_time = time_elapsed_since(epoch_start)[0] valid_loss, valid_time, valid_psnr = self.eval(valid_loader) show_on_epoch_end(epoch_time, valid_time, valid_loss, valid_psnr) # Decrease learning rate if plateau self.scheduler.step(valid_loss) # Save checkpoint stats['train_loss'].append(train_loss) stats['valid_loss'].append(valid_loss) stats['valid_psnr'].append(valid_psnr) self.save_model(epoch, stats, epoch == 0) def test(self, test_loader, show=1): """Evaluates denoiser on test set.""" self.model.train(False) source_imgs = [] denoised_imgs = [] clean_imgs = [] # Create directory for denoised images denoised_dir = os.path.dirname(self.p.data) save_path = os.path.join(denoised_dir, 'denoised') if not os.path.isdir(save_path): os.mkdir(save_path) for batch_idx, (source, target) in enumerate(test_loader): # Only do first <show> images if show == 0 or batch_idx >= show: break source_imgs.append(source) clean_imgs.append(target) if self.use_cuda: source = source.cuda() # Denoise denoised_img = self.model(source).detach() denoised_imgs.append(denoised_img) # Squeeze tensors source_imgs = [t.squeeze(0) for t in source_imgs] denoised_imgs = [t.squeeze(0) for t in denoised_imgs] clean_imgs = [t.squeeze(0) for t in clean_imgs] # Create montage and save images print('Saving images and montages to: {}'.format(save_path)) for i in range(len(source_imgs)): img_name = test_loader.dataset.imgs[i] create_montage(img_name, self.p.noise_type, save_path, source_imgs[i], denoised_imgs[i], clean_imgs[i], show) def eval(self, valid_loader): """Evaluates denoiser on validation set.""" self.model.train(False) valid_start = datetime.now() loss_meter = AvgMeter() psnr_meter = AvgMeter() for batch_idx, (source, target) in enumerate(valid_loader): if self.use_cuda: source = source.cuda() target = target.cuda() # Denoise source_denoised = self.model(source) # Update loss loss = self.loss(source_denoised, target) loss_meter.update(loss.item()) # Compute PSRN if self.is_mc: source_denoised = reinhard_tonemap(source_denoised) # TODO: Find a way to offload to GPU, and deal with uneven batch sizes for i in range(self.p.batch_size): source_denoised = source_denoised.cpu() target = target.cpu() psnr_meter.update(psnr(source_denoised[i], target[i]).item()) valid_loss = loss_meter.avg valid_time = time_elapsed_since(valid_start)[0] psnr_avg = psnr_meter.avg return valid_loss, valid_time, psnr_avg def train(self, train_loader, valid_loader): """Trains denoiser on training set.""" self.model.train(True) self._print_params() num_batches = len(train_loader) assert num_batches % self.p.report_interval == 0, 'Report interval must divide total number of batches' # Dictionaries of tracked stats stats = {'noise_type': self.p.noise_type, 'noise_param': self.p.noise_param, 'train_loss': [], 'valid_loss': [], 'valid_psnr': []} # Main training loop train_start = datetime.now() for epoch in range(self.p.nb_epochs): print('EPOCH {:d} / {:d}'.format(epoch + 1, self.p.nb_epochs)) # Some stats trackers epoch_start = datetime.now() train_loss_meter = AvgMeter() loss_meter = AvgMeter() time_meter = AvgMeter() # Minibatch SGD for batch_idx, (source, target) in enumerate(train_loader): batch_start = datetime.now() progress_bar(batch_idx, num_batches, self.p.report_interval, loss_meter.val) if self.use_cuda: source = source.cuda() target = target.cuda() # Denoise image source_denoised = self.model(source) loss = self.loss(source_denoised, target) loss_meter.update(loss.item()) # Zero gradients, perform a backward pass, and update the weights self.optim.zero_grad() loss.backward() self.optim.step() # Report/update statistics time_meter.update(time_elapsed_since(batch_start)[1]) if (batch_idx + 1) % self.p.report_interval == 0 and batch_idx: show_on_report(batch_idx, num_batches, loss_meter.avg, time_meter.avg) train_loss_meter.update(loss_meter.avg) loss_meter.reset() time_meter.reset() # Epoch end, save and reset tracker self._on_epoch_end(stats, train_loss_meter.avg, epoch, epoch_start, valid_loader) train_loss_meter.reset() train_elapsed = time_elapsed_since(train_start)[0] print('Training done! Total elapsed time: {}\n'.format(train_elapsed))
class Trainer: def __init__(self, seq_length, color_channels, unet_path="pretrained/unet.mdl", discrim_path="pretrained/dicrim.mdl", facenet_path="pretrained/facenet.mdl", vgg_path="", embedding_size=1000, unet_depth=3, unet_filts=32, facenet_filts=32, resnet=18): self.color_channels = color_channels self.margin = 0.5 self.writer = SummaryWriter(log_dir="logs") self.unet_path = unet_path self.discrim_path = discrim_path self.facenet_path = facenet_path self.unet = UNet(in_channels=color_channels, out_channels=color_channels, depth=unet_depth, start_filts=unet_filts, up_mode="upsample", merge_mode='concat').to(device) self.discrim = FaceNetModel(embedding_size=embedding_size, start_filts=facenet_filts, in_channels=color_channels, resnet=resnet, pretrained=False).to(device) self.facenet = FaceNetModel(embedding_size=embedding_size, start_filts=facenet_filts, in_channels=color_channels, resnet=resnet, pretrained=False).to(device) if os.path.isfile(unet_path): self.unet.load_state_dict(torch.load(unet_path)) print("unet loaded") if os.path.isfile(discrim_path): self.discrim.load_state_dict(torch.load(discrim_path)) print("discrim loaded") if os.path.isfile(facenet_path): self.facenet.load_state_dict(torch.load(facenet_path)) print("facenet loaded") if os.path.isfile(vgg_path): self.vgg_loss_network = LossNetwork(vgg_face_dag(vgg_path)).to(device) self.vgg_loss_network.eval() print("vgg loaded") self.mse_loss_function = nn.MSELoss().to(device) self.discrim_loss_function = nn.BCELoss().to(device) self.triplet_loss_function = TripletLoss(margin=self.margin) self.unet_optimizer = torch.optim.Adam(self.unet.parameters(), betas=(0.9, 0.999)) self.discrim_optimizer = torch.optim.Adam(self.discrim.parameters(), betas=(0.9, 0.999)) self.facenet_optimizer = torch.optim.Adam(self.facenet.parameters(), betas=(0.9, 0.999)) def test(self, test_loader, epoch=0): X, y = next(iter(test_loader)) B, D, C, W, H = X.shape # X = X.view(B, C * D, W, H) self.unet.eval() self.facenet.eval() self.discrim.eval() with torch.no_grad(): y_ = self.unet(X.to(device)) mse = self.mse_loss_function(y_, y.to(device)) loss_G = self.loss_GAN_generator(btch_X=X.to(device)) loss_D = self.loss_GAN_discrimator(btch_X=X.to(device), btch_y=y.to(device)) loss_facenet, _, n_bad = self.loss_facenet(X.to(device), y.to(device)) plt.title(f"epoch {epoch} mse={mse.item():.4} facenet={loss_facenet.item():.4} bad={n_bad / B ** 2}") i = np.random.randint(0, B) a = np.hstack((y[i].transpose(0, 1).transpose(1, 2), y_[i].transpose(0, 1).transpose(1, 2).to(cpu))) b = np.hstack((X[i][0].transpose(0, 1).transpose(1, 2), X[i][-1].transpose(0, 1).transpose(1, 2))) plt.imshow(np.vstack((a, b))) plt.axis('off') plt.show() self.writer.add_scalar("test bad_percent", n_bad / B ** 2, global_step=epoch) self.writer.add_scalar("test loss", mse.item(), global_step=epoch) # self.writer.add_scalars("test GAN", {"discrim": loss_D.item(), # "gen": loss_G.item()}, global_step=epoch) with torch.no_grad(): n_for_show = 10 y_show_ = y_.to(device) y_show = y.to(device) embeddings_anc, _ = self.facenet(y_show_) embeddings_pos, _ = self.facenet(y_show) embeds = torch.cat((embeddings_anc[:n_for_show], embeddings_pos[:n_for_show])) imgs = torch.cat((y_show_[:n_for_show], y_show[:n_for_show])) names = list(range(n_for_show)) * 2 # print(embeds.shape, imgs.shape, len(names)) # self.writer.add_embedding(mat=embeds, metadata=names, label_img=imgs, tag="embeddings", global_step=epoch) trshs, fprs, tprs = roc_curve(embeddings_anc.detach().to(cpu), embeddings_pos.detach().to(cpu)) rnk1 = rank1(embeddings_anc.detach().to(cpu), embeddings_pos.detach().to(cpu)) plt.step(fprs, tprs) # plt.xlim((1e-4, 1)) plt.yticks(np.arange(0, 1, 0.05)) plt.xticks(np.arange(min(fprs), max(fprs), 10)) plt.xscale('log') plt.title(f"ROC auc={auc(fprs, tprs)} rnk1={rnk1}") self.writer.add_figure("ROC test", plt.gcf(), global_step=epoch) self.writer.add_scalar("auc", auc(fprs, tprs), global_step=epoch) self.writer.add_scalar("rank1", rnk1, global_step=epoch) print(f"\n###### {epoch} TEST mse={mse.item():.4} GAN(G/D)={loss_G.item():.4}/{loss_D.item():.4} " f"facenet={loss_facenet.item():.4} bad={n_bad / B ** 2:.4} auc={auc(fprs, tprs)} rank1={rnk1} #######") def test_test(self, test_loader): X, ys = next(iter(test_loader)) true_idx = 0 x = X[true_idx] D, C, W, H = x.shape # x = x.view(C * D, W, H) dists = list() with torch.no_grad(): y_ = self.unet(x.to(device)) embedding_anc, _ = self.facenet(y_) embeddings_pos, _ = self.facenet(ys) for emb_pos_item in embeddings_pos: dist = l2_dist.forward(embedding_anc, emb_pos_item) dists.append(dist) a_sorted = np.argsort(dists) a = np.hstack((ys[true_idx].transpose(0, 1).transpose(1, 2), y_.transpose(0, 1).transpose(1, 2).to(cpu).numpy(), ys[a_sorted[0]].transpose(0, 1).transpose(1, 2))) b = np.hstack((x[0:3].transpose(0, 1).transpose(1, 2), x[D // 2 * C:D // 2 * C + 3].transpose(0, 1).transpose(1, 2), x[-3:].transpose(0, 1).transpose(1, 2))) b_ = b - np.min(b) b_ = b_ / np.max(b) b_ = equalize_func([(b_ * 255).astype(np.uint8)], use_clahe=True)[0] b = b_.astype(np.float32) / 255 plt.imshow(cv2.cvtColor(np.vstack((a, b)), cv2.COLOR_BGR2RGB)) plt.axis('off') plt.show() def loss_facenet(self, X, y, is_detached=False): B, D, C, W, H = X.shape y_ = self.unet(X) embeddings_anc, D_fake = self.facenet(y_ if not is_detached else y_.detach()) embeddings_pos, D_real = self.facenet(y) target_real = torch.full_like(D_fake, 1) loss_gen = self.discrim_loss_function(D_fake, target_real) pos_dist = l2_dist.forward(embeddings_anc, embeddings_pos) bad_triplets_loss = None n_bad = 0 for shift in range(1, B): embeddings_neg = torch.roll(embeddings_pos, shift, 0) neg_dist = l2_dist.forward(embeddings_anc, embeddings_neg) bad_triplets_idxs = np.where((neg_dist - pos_dist < self.margin).cpu().numpy().flatten())[0] if shift == 1: bad_triplets_loss = self.triplet_loss_function.forward(embeddings_anc[bad_triplets_idxs], embeddings_pos[bad_triplets_idxs], embeddings_neg[bad_triplets_idxs]).to( device) else: bad_triplets_loss += self.triplet_loss_function.forward(embeddings_anc[bad_triplets_idxs], embeddings_pos[bad_triplets_idxs], embeddings_neg[bad_triplets_idxs]).to(device) n_bad += len(bad_triplets_idxs) bad_triplets_loss /= B return bad_triplets_loss, torch.mean(loss_gen), n_bad # def loss_mse(self, btch_X, btch_y): # btch_y_ = self.unet(btch_X) # loss_unet = self.mse_loss_function(btch_y_, btch_y) # # features_target = self.facenet.forward_mse(btch_y) # features = self.facenet.forward_mse(btch_y_) # # loss_first_layer = self.mse_loss_function(features, features_target) # return loss_unet + loss_first_layer def loss_mse_vgg(self, btch_X, btch_y, k_mse, k_vgg): btch_y_ = self.unet(btch_X) # print(btch_y_.shape,btch_y.shape) perceptual_btch_y_ = self.vgg_loss_network(btch_y_) perceptual_btch_y = self.vgg_loss_network(btch_y) perceptual_loss = 0.0 for a, b in zip(perceptual_btch_y_, perceptual_btch_y): perceptual_loss += self.mse_loss_function(a, b) return k_vgg * perceptual_loss + k_mse * self.mse_loss_function(btch_y_, btch_y) def loss_GAN_discrimator(self, btch_X, btch_y): btch_y_ = self.unet(btch_X) _, y_D_fake_ = self.discrim(btch_y_.detach()) _, y_D_real_ = self.discrim(btch_y) target_fake = torch.full_like(y_D_fake_, 0) target_real = torch.full_like(y_D_real_, 1) loss_D_fake_ = self.discrim_loss_function(y_D_fake_, target_fake) loss_D_real_ = self.discrim_loss_function(y_D_real_, target_real) loss_discrim = (loss_D_real_ + loss_D_fake_) return loss_discrim def loss_GAN_generator(self, btch_X): btch_y_ = self.unet(btch_X) _, y_D_fake_ = self.discrim(btch_y_) target_real = torch.full_like(y_D_fake_, 1) loss_gen = self.discrim_loss_function(y_D_fake_, target_real) return loss_gen def relax_discriminator(self, btch_X, btch_y): self.discrim.zero_grad() # train with real y_discrim_real_ = self.discrim(btch_y) y_discrim_real_ = y_discrim_real_.mean() y_discrim_real_.backward(self.mone) # train with fake btch_y_ = self.unet(btch_X) y_discrim_fake_detached_ = self.discrim(btch_y_.detach()) y_discrim_fake_detached_ = y_discrim_fake_detached_.mean() y_discrim_fake_detached_.backward(self.one) # gradient_penalty gradient_penalty = self.discrim_gradient_penalty(btch_y, btch_y_) gradient_penalty.backward() self.discrim_optimizer.step() def relax_generator(self, btch_X): self.unet.zero_grad() btch_y_ = self.unet(btch_X) y_discrim_fake_ = self.discrim(btch_y_) y_discrim_fake_ = y_discrim_fake_.mean() y_discrim_fake_.backward(self.mone) self.unet_optimizer.step() def discrim_gradient_penalty(self, real_y, fake_y): lambd = 10 btch_size = real_y.shape[0] alpha = torch.rand(btch_size, 1, 1, 1).to(device) # print(alpha.shape, real_y.shape) alpha = alpha.expand_as(real_y) interpolates = alpha * real_y + (1 - alpha) * fake_y interpolates = interpolates.to(device) interpolates = autograd.Variable(interpolates, requires_grad=True) interpolates_out = self.discrim(interpolates) gradients = autograd.grad(outputs=interpolates_out, inputs=interpolates, grad_outputs=torch.ones(interpolates_out.size()).to(device), create_graph=True, retain_graph=True, only_inputs=True)[0] gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean() * lambd return gradient_penalty def train(self, train_loader, test_loader, batch_size=2, epochs=30, k_gen=1, k_discrim=1, k_mse=1, k_facenet=1, k_facenet_back=1, k_vgg=1): """ :param X: np.array shape=(n_videos, n_frames, h, w) :param y: np.array shape=(n_videos, h, w) :param epochs: int """ print("\nSTART TRAINING\n") for epoch in range(epochs): self.test(test_loader, epoch) self.unet.train() self.facenet.train() self.discrim.train() # train by batches for idx, (btch_X, btch_y) in enumerate(train_loader): B, D, C, W, H = btch_X.shape # btch_X = btch_X.view(B, C * D, W, H) btch_X = btch_X.to(device) btch_y = btch_y.to(device) # Mse loss self.unet.zero_grad() mse = self.loss_mse_vgg(btch_X, btch_y, k_mse, k_vgg) mse.backward() self.unet_optimizer.step() # facenet_backup = deepcopy(self.facenet.state_dict()) # for i in range(unrolled_iterations): self.discrim.zero_grad() loss_D = self.loss_GAN_discrimator(btch_X, btch_y) loss_D = k_discrim * loss_D loss_D.backward() self.discrim_optimizer.step() self.discrim.zero_grad() self.unet.zero_grad() loss_G = self.loss_GAN_generator(btch_X) loss_G = k_gen * loss_G loss_G.backward() self.unet_optimizer.step() # Facenet self.unet.zero_grad() self.facenet.zero_grad() facenet_loss, _, n_bad = self.loss_facenet(btch_X, btch_y) facenet_loss = k_facenet * facenet_loss facenet_loss.backward() self.facenet_optimizer.step() self.unet.zero_grad() self.facenet.zero_grad() facenet_back_loss, _, n_bad = self.loss_facenet(btch_X, btch_y) facenet_back_loss = k_facenet_back * facenet_back_loss facenet_back_loss.backward() self.unet_optimizer.step() print(f"btch {idx * batch_size} mse={mse.item():.4} GAN(G/D)={loss_G.item():.4}/{loss_D.item():.4} " f"facenet={facenet_loss.item():.4} bad={n_bad / B ** 2:.4}") global_step = epoch * len(train_loader.dataset) // batch_size + idx self.writer.add_scalar("train bad_percent", n_bad / B ** 2, global_step=global_step) self.writer.add_scalar("train loss", mse.item(), global_step=global_step) # self.writer.add_scalars("train GAN", {"discrim": loss_D.item(), # "gen": loss_G.item()}, global_step=global_step) torch.save(self.unet.state_dict(), self.unet_path) torch.save(self.discrim.state_dict(), self.discrim_path) torch.save(self.facenet.state_dict(), self.facenet_path)
shuffle=False, num_workers=4) if args.model == "unet": model = UNet(input_channels=NUM_INPUT_CHANNELS, output_channels=NUM_OUTPUT_CHANNELS) elif args.model == "segnet": model = SegNet(input_channels=NUM_INPUT_CHANNELS, output_hannels=NUM_OUTPUT_CHANNELS) else: model = PSPNet(layers=50, bins=(1, 2, 3, 6), dropout=0.1, classes=NUM_OUTPUT_CHANNELS, use_ppm=True, pretrained=True) class_weights = 1.0/train_dataset.get_class_probability() criterion = torch.nn.CrossEntropyLoss(weight=class_weights) if CUDA: model = model.cuda(GPU_ID) class_weights = class_weights.cuda(GPU_ID) criterion = criterion.cuda(GPU_ID) if args.checkpoint: model.load_state_dict(torch.load(args.checkpoint)) optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE) train()
def train(): startTime = time.time() args = parameters.parse_arguments() logging.basicConfig(filename=args.logfile, level=logging.INFO) logging.critical("\n\n" + args.log_header) logging.info(args) device = ("cuda" if torch.cuda.is_available() else "cpu") logging.info(f"TIME: {time.time() - startTime}s Using device {device}") logging.info(f"TIME: {time.time()-startTime}s Loading dataset") try: with open(os.path.join(args.datadir, "data.pkl"), "rb") as f: data = pickle.load(f) except: data = DataLoader(args.datadir, int(args.batchsize), shuffle=int(args.shuffle)) with open(os.path.join(args.datadir, "data.pkl"), "wb") as f: pickle.dump(data, f) data.batchSize = int(args.batchsize) logging.info(f"TIME: {time.time()-startTime}s Dataset Loaded") random.seed(args.seed) indices = list(range(len(data))) random.shuffle( indices ) # 0:floor((1-validationFrac)*len(data)) will be training data, rest will be validation data trainEndIndex = math.floor((1 - args.validation_frac) * (len(data))) model = UNet(in_channels=1, num_classes=2, start_filts=int(args.conv_filters), up_mode=args.mode, depth=int(args.depth), batchnorm=args.batchnorm) model.reset_params() model = model.to(device) optimizer = None if args.optimizer == 'adam': optimizer = optim.Adam(model.parameters(), lr=args.lrstart) logging.info(f"TIME: {time.time()-startTime}s Optimizer: adam") elif args.optimizer == 'sgd': optimizer = optim.SGD(model.parameters(), lr=args.lrstart, momentum=args.momentum) logging.info(f"TIME: {time.time()-startTime}s Optimizer: SGD") elif args.optimizer == 'rmsprop': optimizer = optim.RMSprop(model.parameters(), lr=args.lrstart) logging.info(f"TIME: {time.time()-startTime}s Optimizer: RMSProp") else: logging.error( f"TIME: {time.time()-startTime}s Incorrect optimizer given") scheduler = [] if args.lrscheduler == "steplr": scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=args.decay) logging.info(f"TIME: {time.time()-startTime}s LRScheduler: StepLR") elif args.lrscheduler == "exponentiallr": scheduler = optim.lr_scheduler.ExponentialLR(optimizer, gamma=args.decay) logging.info( f"TIME: {time.time()-startTime}s LRScheduler: exponentialLR") else: scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=int(args.epochs)) logging.info( f"TIME: {time.time()-startTime}s LRScheduler: lr shouldn't change with epochs" ) criteria = CombinedLoss(args.lambda_loss, args.loss_type) diceCoeff = DiceLoss() TL = [] VL = [] if not os.path.exists(os.path.join(os.getcwd(), "loss_files")): os.makedirs(os.path.join(os.getcwd(), "loss_files")) lossFile = open(os.path.join("loss_files", args.log_header + ".csv"), "w+") lossFile.write("Epoch,TrainLoss,ValidationLoss,Dice Coefficient\n") for epoch in tqdm(range(1, int(args.epochs) + 1), desc="Training model"): trainLoss = 0 valLoss = 0 trainingSample = 0 testSample = 0 netCoeff = 0 for i in range(len(data)): images, masks = data[i] images = torch.tensor(images.astype(np.float32)) masks = torch.tensor(masks.astype(np.float32)) images = images.to(device) masks = masks.to(device) images = torch.transpose(images, 1, 3) masks = torch.transpose(masks, 1, 3) if i in indices[:trainEndIndex]: trainingSample += images.shape[0] networkPred = model(images) if args.regularization == 'l1': reg = L1_regularization(model, args.reg_lamda1) loss = criteria(masks, networkPred) + reg elif args.regularization == 'l1l2': reg = L1L2_regularization(model, args.reg_lamda1, args.reg_lamda2) loss = criteria(masks, networkPred) + reg else: loss = criteria(masks, networkPred) loss.backward() trainLoss += loss.item() optimizer.step() model.zero_grad() else: with torch.no_grad(): testSample += images.shape[0] prediction = model(images) if (epoch % args.save_epochs == 0) or (epoch == 1) or (epoch == args.epochs): imgPath = os.path.join("validation_sample", args.log_header, f"epoch {epoch}") if not os.path.exists(imgPath): os.makedirs(imgPath) hrt = images[0, 0, :, :].to("cpu") plt.imshow(np.array(hrt), cmap='gray') plt.title("Heart Image") plt.savefig(os.path.join(imgPath, "heart.png")) plt.clf() # ax = figure.add_subplot(232, title="Mask 1 Predicted") msk1 = prediction[0, 0, :, :].to("cpu") plt.imshow(np.array(msk1), cmap='gray') plt.title("Predicted Mask 1") plt.savefig(os.path.join(imgPath, "pred-mask1.png")) plt.clf() # ax = figure.add_subplot(231, title="Mask 2 Predicted") msk2 = prediction[0, 1, :, :].to("cpu") plt.imshow(np.array(msk2), cmap='gray') plt.title("Predicted Mask 2") plt.savefig(os.path.join(imgPath, "pred-mask2.png")) plt.clf() msk = np.zeros((192, 192, 3)) msk[:, :, 0] = np.array(msk1) msk[:, :, 1] = np.array(msk2) plt.imshow(np.array(hrt), cmap='gray') plt.imshow(msk, cmap='jet', alpha=0.4) plt.title("predicted-RV") plt.savefig(os.path.join(imgPath, "pred-RV.png")) plt.clf() # ax = figure.add_subplot(231, title="Mask 2 Real") msk1 = masks[0, 0, :, :].to("cpu") plt.imshow(np.array(msk1), cmap='gray') plt.title("Actual Mask 1") plt.savefig(os.path.join(imgPath, "actual-mask1.png")) plt.clf() # ax = figure.add_subplot(231, title="Mask 2 Real") msk2 = masks[0, 1, :, :].to("cpu") plt.imshow(np.array(msk2), cmap='gray') plt.title("Actual Mask 2") plt.savefig(os.path.join(imgPath, "actual-mask2.png")) plt.clf() # plt.savefig(os.path.join("validation_sample", f"{args.log_header}-epoch {epoch}.png")) msk = np.zeros((192, 192, 3)) msk[:, :, 0] = np.array(msk1) msk[:, :, 1] = np.array(msk2) plt.imshow(np.array(hrt), cmap='gray') plt.imshow(msk, cmap='jet', alpha=0.4) plt.title("actual-RV") plt.savefig(os.path.join(imgPath, "actual-RV.png")) plt.clf() if args.regularization == 'l1': reg = L1_regularization(model, args.reg_lamda1) loss = criteria(masks, prediction) + reg elif args.regularization == 'l1l2': reg = L1L2_regularization(model, args.reg_lamda1, args.reg_lamda2) loss = criteria(masks, prediction) + reg else: loss = criteria(masks, prediction) valLoss += loss.item() coeff = diceCoeff(masks, prediction) netCoeff += torch.sum(1 - coeff).item() if (epoch % int(args.save_epochs) == 0) or (epoch == int(args.epochs)): if not os.path.exists(args.model_save_dir): os.makedirs(args.model_save_dir) # save model torch.save( { "epoch": epoch, "model_state_dict": model.state_dict(), "optimizer_state_dict": optimizer.state_dict(), }, os.path.join(args.model_save_dir, f"model-epoch({epoch}).hdf5")) logging.info( f"TIME: {time.time()-startTime}s Model state saved for epoch: {epoch}" ) logging.info( f"TIME: {time.time()-startTime}s TRAINING: Epoch: {epoch}, lr: {scheduler.get_last_lr()}, loss: {trainLoss/(2*trainingSample)}" ) logging.info( f"TIME: {time.time()-startTime}s VALIDATION: Epoch: {epoch}, lr: {scheduler.get_last_lr()}, loss: {valLoss/(2*testSample)}" ) TL.append(trainLoss / (2 * trainingSample)) VL.append(valLoss / (2 * testSample)) lossFile.write( f"{epoch},{trainLoss/(2*trainingSample)},{valLoss/(2*testSample)},{netCoeff/(2*testSample)}\n" ) scheduler.step( ) # https://www.deeplearningwizard.com/deep_learning/boosting_models_pytorch/lr_scheduling/ plt.plot(list(range(1, int(args.epochs) + 1)), TL, label="Training loss") plt.plot(list(range(1, int(args.epochs) + 1)), VL, label="Validation loss") plt.xlabel("Epoch") plt.ylabel("Loss") plt.legend(loc="best") if not os.path.exists(os.path.join(os.getcwd(), "plots")): os.makedirs(os.path.join(os.getcwd(), "plots")) plt.savefig(os.path.join("plots", args.log_header + ".png"))
def train(input_data_type, grade, seg_type, num_classes, batch_size, epochs, use_gpu, learning_rate, w_decay, pre_trained=False): logger.info('Start training using {} modal.'.format(input_data_type)) model = UNet(4, 4, residual=True, expansion=2) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(params=model.parameters(), lr=learning_rate, weight_decay=w_decay) if pre_trained: checkpoint = torch.load(pre_trained_path, map_location=device) model.load_state_dict(checkpoint['model_state_dict']) if use_gpu: ts = time.time() model.to(device) print("Finish cuda loading, time elapsed {}".format(time.time() - ts)) scheduler = lr_scheduler.StepLR( optimizer, step_size=step_size, gamma=gamma) # decay LR by a factor of 0.5 every 5 epochs data_set, data_loader = get_dataset_dataloader(input_data_type, seg_type, batch_size, grade=grade) since = time.time() best_model_wts = copy.deepcopy(model.state_dict()) best_iou = 0.0 epoch_loss = np.zeros((2, epochs)) epoch_acc = np.zeros((2, epochs)) epoch_class_acc = np.zeros((2, epochs)) epoch_mean_iou = np.zeros((2, epochs)) evaluator = Evaluator(num_classes) def term_int_handler(signal_num, frame): np.save(os.path.join(score_dir, 'epoch_accuracy'), epoch_acc) np.save(os.path.join(score_dir, 'epoch_mean_iou'), epoch_mean_iou) np.save(os.path.join(score_dir, 'epoch_loss'), epoch_loss) model.load_state_dict(best_model_wts) logger.info('Got terminated and saved model.state_dict') torch.save(model.state_dict(), os.path.join(score_dir, 'terminated_model.pt')) torch.save( { 'model_state_dict': model.state_dict(), 'optimizer_state_dict': optimizer.state_dict() }, os.path.join(score_dir, 'terminated_model.tar')) quit() signal.signal(signal.SIGINT, term_int_handler) signal.signal(signal.SIGTERM, term_int_handler) for epoch in range(epochs): logger.info('Epoch {}/{}'.format(epoch + 1, epochs)) logger.info('-' * 28) for phase_ind, phase in enumerate(['train', 'val']): if phase == 'train': model.train() logger.info(phase) else: model.eval() logger.info(phase) evaluator.reset() running_loss = 0.0 running_dice = 0.0 for batch_ind, batch in enumerate(data_loader[phase]): imgs, targets = batch imgs = imgs.to(device) targets = targets.to(device) # zero the learnable parameters gradients optimizer.zero_grad() with torch.set_grad_enabled(phase == 'train'): outputs = model(imgs) loss = criterion(outputs, targets) if phase == 'train': loss.backward() optimizer.step() preds = torch.argmax(F.softmax(outputs, dim=1), dim=1, keepdim=True) running_loss += loss * imgs.size(0) logger.debug('Batch {} running loss: {:.4f}'.format(batch_ind,\ running_loss)) # test the iou and pixelwise accuracy using evaluator preds = torch.squeeze(preds, dim=1) preds = preds.cpu().numpy() targets = targets.cpu().numpy() evaluator.add_batch(targets, preds) epoch_loss[phase_ind, epoch] = running_loss / len(data_set[phase]) epoch_acc[phase_ind, epoch] = evaluator.Pixel_Accuracy() epoch_class_acc[phase_ind, epoch] = evaluator.Pixel_Accuracy_Class() epoch_mean_iou[phase_ind, epoch] = evaluator.Mean_Intersection_over_Union() logger.info('{} loss: {:.4f}, acc: {:.4f}, class acc: {:.4f}, mean iou: {:.6f}'.format(phase,\ epoch_loss[phase_ind, epoch],\ epoch_acc[phase_ind, epoch],\ epoch_class_acc[phase_ind, epoch],\ epoch_mean_iou[phase_ind, epoch])) if phase == 'val' and epoch_mean_iou[phase_ind, epoch] > best_iou: best_iou = epoch_mean_iou[phase_ind, epoch] best_model_wts = copy.deepcopy(model.state_dict()) if phase == 'val' and (epoch + 1) % 10 == 0: logger.info('Saved model.state_dict in epoch {}'.format(epoch + 1)) torch.save( model.state_dict(), os.path.join(score_dir, 'epoch{}_model.pt'.format(epoch + 1))) print() time_elapsed = time.time() - since logger.info('Training completed in {}m {}s'.format(int(time_elapsed / 60),\ int(time_elapsed) % 60)) # load best model weights model.load_state_dict(best_model_wts) # save numpy results np.save(os.path.join(score_dir, 'epoch_accuracy'), epoch_acc) np.save(os.path.join(score_dir, 'epoch_mean_iou'), epoch_mean_iou) np.save(os.path.join(score_dir, 'epoch_loss'), epoch_loss) return model, optimizer
def denoising_fixd_noise_level(noise_im, clean_im, LR=1e-2, sigma=5, rho=1, eta=0.5, alpha=0.5, total_step=19, prob1_iter=1000, noise_level=None, result_root=None, fo=None): sig = noise_level r_nlm = denoise_nl_means(noise_im.transpose(1, 2, 0), h=0.8 * sig, sigma=sig, fast_mode=False, **patch_kw) r_nlm = np.clip(r_nlm, 0, 255) psnr_nlm = compare_psnr(clean_im.transpose(1, 2, 0), r_nlm, 255) ssim_nlm = compare_ssim(r_nlm, clean_im.transpose(1, 2, 0), multichannel=True, data_range=255) print('noise level {} '.format(noise_level), file=fo, flush=True) print('PSNR_NLM: {}, SSIM_NLM: {}'.format(psnr_nlm, ssim_nlm), file=fo, flush=True) r_nlm = Image.fromarray(r_nlm.astype(np.uint8)) r_nlm.save(result_root + 'nlm_result.png') input_depth = 3 latent_dim = 3 en_net = UNet(input_depth, latent_dim, need_sigmoid=True).cuda() de_net = UNet(latent_dim, input_depth, need_sigmoid=True).cuda() en_optimizer = torch.optim.Adam(en_net.parameters(), lr=LR) de_optimizer = torch.optim.Adam(de_net.parameters(), lr=LR) l2_loss = torch.nn.MSELoss().cuda() noise_im = noise_im / 255.0 i0 = torch.Tensor(noise_im[None, ...]).cuda() noise_im_torch = torch.Tensor(noise_im)[None, ...].cuda() Y = torch.zeros_like(noise_im_torch).cuda() i0_til_torch = torch.Tensor(noise_im[None, ...]).cuda() output = None for i in range(total_step): ############################### sub-problem 1 ################################# for i_1 in range(prob1_iter): mean_i = en_net(noise_im_torch) eps = mean_i.clone().normal_() out = de_net(mean_i + eps) total_loss = 0.5 * l2_loss(out, noise_im_torch) total_loss += 1 / (2 * sigma**2) * l2_loss(mean_i, i0) en_optimizer.zero_grad() de_optimizer.zero_grad() total_loss.backward() en_optimizer.step() de_optimizer.step() with torch.no_grad(): i0 = ((1 / sigma**2) * mean_i + rho * (i0_til_torch - Y) + alpha * noise_im_torch) / ( (1 / sigma**2) + rho + alpha) with torch.no_grad(): ############################### sub-problem 2 ################################# i0_np = i0.cpu().squeeze().detach().numpy() Y_np = Y.cpu().squeeze().detach().numpy() tmp = i0_np.transpose(1, 2, 0) + Y_np.transpose(1, 2, 0) tmp = np.clip(tmp, 0, 1) sig = noise_level i0_til_np = denoise_nl_means(tmp * 255, h=0.8 * sig, sigma=sig, fast_mode=False, **patch_kw) i0_til_np = i0_til_np / 255 i0_til_torch = torch.Tensor( i0_til_np.transpose(2, 0, 1)[None, ...]).cuda() ############################### sub-problem 3 ################################# Y = Y + eta * (i0 - i0_til_torch) ############################################################################### i0_til_np = i0_til_torch.cpu().squeeze().detach().numpy() i0_til_np = np.clip(i0_til_np, 0, 1) output = i0_til_np psnr_gt = compare_psnr(clean_im.transpose(1, 2, 0), 255 * i0_til_np.transpose(1, 2, 0), 255) ssim_gt = compare_ssim(255 * i0_til_np.transpose(1, 2, 0), clean_im.transpose(1, 2, 0), multichannel=True, data_range=255) if not i % 5: denoise_obj_pil = Image.fromarray((tmp * 255).astype(np.uint8)) Y_np = Y.cpu().squeeze().detach().numpy() i0_np = np.clip(i0_np, 0, 1) i0_pil = Image.fromarray( np.uint8(255 * i0_np.transpose(1, 2, 0))) i0_til_np = i0_til_np.transpose(1, 2, 0) i0_til_pil = Image.fromarray( (255 * i0_til_np).astype(np.uint8)) mean_i_np = mean_i.cpu().squeeze().detach().numpy().clip(0, 1) mean_i_pil = Image.fromarray( (255 * mean_i_np.transpose(1, 2, 0)).astype(np.uint8)) out_np = out.cpu().squeeze().detach().numpy().clip(0, 1) out_pil = Image.fromarray( (255 * out_np.transpose(1, 2, 0)).astype(np.uint8)) denoise_obj_name = 'denoise_obj_{:04d}'.format(i) + '.png' i0_name = 'i0_num_epoch_{:04d}'.format(i) + '.png' result_name = 'num_epoch_{:04d}'.format(i) + '.png' mean_i_name = 'Latent_im_num_epoch_{:04d}'.format(i) + '.png' out_name = 'res_of_dec_num_epoch_{:04d}'.format(i) + '.png' denoise_obj_pil.save(result_root + denoise_obj_name) i0_pil.save(result_root + i0_name) i0_til_pil.save(result_root + result_name) mean_i_pil.save(result_root + mean_i_name) out_pil.save(result_root + out_name) print('Iteration %05d Loss %f PSNR_gt: %f SSIM_gt: %f' % (i, total_loss.item(), psnr_gt, ssim_gt), file=fo, flush=True) psnr = psnr_gt ssim = ssim_gt output_pil = Image.fromarray( (255 * output.transpose(1, 2, 0)).astype(np.uint8)) output_pil.save(result_root + 'ours_result.png') return psnr, ssim, psnr_nlm, ssim_nlm
model = UNet(input_channels=NUM_INPUT_CHANNELS, output_channels=NUM_OUTPUT_CHANNELS) elif args.model == "segnet": model = SegNet(input_channels=NUM_INPUT_CHANNELS, output_hannels=NUM_OUTPUT_CHANNELS) else: model = PSPNet( layers=50, bins=(1, 2, 3, 6), dropout=0.1, classes=NUM_OUTPUT_CHANNELS, use_ppm=True, pretrained=True, ) # class_weights = 1.0 / train_dataset.get_class_probability() # criterion = torch.nn.CrossEntropyLoss(weight=class_weights) criterion = torch.nn.CrossEntropyLoss() if CUDA: model = model.cuda(device=GPU_ID) # class_weights = class_weights.cuda(GPU_ID) criterion = criterion.cuda(device=GPU_ID) if args.checkpoint: model.load_state_dict(torch.load(args.checkpoint)) optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE) train()
val_size = len(datasets) - train_size train_dataset, val_dataset = torch.utils.data.random_split( datasets, [train_size, val_size]) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=4) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=4) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = UNet(n_channels=3, n_classes=21).to(device) criterion = BCEDiceLoss().to(device) model_fcn8 = fcn8s().to(device) criterion_fcn8 = BCEDiceLoss().to(device) model_segnet = segnet().to(device) criterion_segnet = BCEDiceLoss().to(device) optimizer = optim.Adam(model.parameters(), lr=0.001) optimizer_fcn8 = optim.Adam(model_fcn8.parameters(), lr=0.001) optimizer_segnet = optim.Adam(model_segnet.parameters(), lr=0.001) # colabは相対パスがいいみたい # logdir = "logs" # logdir_path = os.path.join(base_path, logdir) logdir_path = "./logs" if not os.path.isdir(logdir_path): os.mkdir(logdir_path) dt = datetime.datetime.now() # log writer for unet model_id = len( glob.glob( os.path.join(logdir_path, "{}{}{}*".format(dt.year, dt.month,
def main(): parser = argparse.ArgumentParser(description="Train the model") parser.add_argument('-trainf', "--train-filepath", type=str, default=None, required=True, help="training dataset filepath.") parser.add_argument('-validf', "--val-filepath", type=str, default=None, help="validation dataset filepath.") parser.add_argument("--shuffle", action="store_true", default=False, help="Shuffle the dataset") parser.add_argument("--load-weights", type=str, default=None, help="load pretrained weights") parser.add_argument("--load-model", type=str, default=None, help="load pretrained model, entire model (filepath, default: None)") parser.add_argument("--debug", action="store_true", default=False) parser.add_argument('--epochs', type=int, default=30, help='number of epochs to train (default: 30)') parser.add_argument("--batch-size", type=int, default=32, help="Batch size") parser.add_argument('--img-shape', type=str, default="(1,512,512)", help='Image shape (default "(1,512,512)"') parser.add_argument("--num-cpu", type=int, default=10, help="Number of CPUs to use in parallel for dataloader.") parser.add_argument('--cuda', type=int, default=0, help='CUDA visible device (use CPU if -1, default: 0)') parser.add_argument('--cuda-non-deterministic', action='store_true', default=False, help="sets flags for non-determinism when using CUDA (potentially fast)") parser.add_argument('-lr', type=float, default=0.0005, help='Learning rate') parser.add_argument('--seed', type=int, default=0, help='Seed (numpy and cuda if GPU is used.).') parser.add_argument('--log-dir', type=str, default=None, help='Save the results/model weights/logs under the directory.') args = parser.parse_args() # TODO: support image reshape img_shape = tuple(map(int, args.img_shape.strip()[1:-1].split(","))) if args.log_dir: os.makedirs(args.log_dir, exist_ok=True) best_model_path = os.path.join(args.log_dir, "model_weights.pth") else: best_model_path = None if args.seed is not None: np.random.seed(args.seed) torch.manual_seed(args.seed) if args.cuda >= 0: if args.cuda_non_deterministic: printBlue("Warning: using CUDA non-deterministc. Could be faster but results might not be reproducible.") else: printBlue("Using CUDA deterministc. Use --cuda-non-deterministic might accelerate the training a bit.") # Make CuDNN Determinist torch.backends.cudnn.deterministic = not args.cuda_non_deterministic # torch.cuda.manual_seed(args.seed) torch.cuda.manual_seed_all(args.seed) # TODO [OPT] enable multi-GPUs ? # https://pytorch.org/tutorials/beginner/former_torchies/parallelism_tutorial.html device = torch.device("cuda:{}".format(args.cuda) if torch.cuda.is_available() and (args.cuda >= 0) else "cpu") # ================= Build dataloader ================= # DataLoader # transform_normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], # std=[0.5, 0.5, 0.5]) transform_normalize = transforms.Normalize(mean=[0.5], std=[0.5]) # Warning: DO NOT use geometry transform (do it in the dataloader instead) data_transform = transforms.Compose([ # transforms.ToPILImage(mode='F'), # mode='F' for one-channel image # transforms.Resize((256, 256)) # NO # transforms.RandomResizedCrop(256), # NO # transforms.RandomHorizontalFlip(p=0.5), # NO # WARNING, ISSUE: transforms.ColorJitter doesn't work with ToPILImage(mode='F'). # Need custom data augmentation functions: TODO: DONE. # transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2), # Use OpenCVRotation, OpenCVXXX, ... (our implementation) # OpenCVRotation((-10, 10)), # angles (in degree) transforms.ToTensor(), # already done in the dataloader transform_normalize ]) geo_transform = GeoCompose([ OpenCVRotation(angles=(-10, 10), scales=(0.9, 1.1), centers=(-0.05, 0.05)), # TODO add more data augmentation here ]) def worker_init_fn(worker_id): # WARNING spawn start method is used, # worker_init_fn cannot be an unpicklable object, e.g., a lambda function. # A work-around for issue #5059: https://github.com/pytorch/pytorch/issues/5059 np.random.seed() data_loader_train = {'batch_size': args.batch_size, 'shuffle': args.shuffle, 'num_workers': args.num_cpu, # 'sampler': balanced_sampler, 'drop_last': True, # for GAN-like 'pin_memory': False, 'worker_init_fn': worker_init_fn, } data_loader_valid = {'batch_size': args.batch_size, 'shuffle': False, 'num_workers': args.num_cpu, 'drop_last': False, 'pin_memory': False, } train_set = LiTSDataset(args.train_filepath, dtype=np.float32, geometry_transform=geo_transform, # TODO enable data augmentation pixelwise_transform=data_transform, ) valid_set = LiTSDataset(args.val_filepath, dtype=np.float32, pixelwise_transform=data_transform, ) dataloader_train = torch.utils.data.DataLoader(train_set, **data_loader_train) dataloader_valid = torch.utils.data.DataLoader(valid_set, **data_loader_valid) # =================== Build model =================== # TODO: control the model by bash command if args.load_weights: model = UNet(in_ch=1, out_ch=3, # there are 3 classes: 0: background, 1: liver, 2: tumor depth=4, start_ch=32, # 64 inc_rate=2, kernel_size=5, # 3 padding=True, batch_norm=True, spec_norm=False, dropout=0.5, up_mode='upconv', include_top=True, include_last_act=False, ) printYellow(f"Loading pretrained weights from: {args.load_weights}...") model.load_state_dict(torch.load(args.load_weights)) printYellow("+ Done.") elif args.load_model: # load entire model model = torch.load(args.load_model) printYellow("Successfully loaded pretrained model.") model.to(device) optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.95)) # TODO best_valid_loss = float('inf') # TODO TODO: add learning decay for epoch in range(args.epochs): for valid_mode, dataloader in enumerate([dataloader_train, dataloader_valid]): n_batch_per_epoch = len(dataloader) if args.debug: n_batch_per_epoch = 1 # infinite dataloader allows several update per iteration (for special models e.g. GAN) dataloader = infinite_dataloader(dataloader) if valid_mode: printYellow("Switch to validation mode.") model.eval() prev_grad_mode = torch.is_grad_enabled() torch.set_grad_enabled(False) else: model.train() st = time.time() cum_loss = 0 for iter_ind in range(n_batch_per_epoch): supplement_logs = "" # reset cumulated losses at the begining of each batch # loss_manager.reset_losses() # TODO: use torch.utils.tensorboard !! optimizer.zero_grad() img, msk = next(dataloader) img, msk = img.to(device), msk.to(device) # TODO this is ugly: convert dtype and convert the shape from (N, 1, 512, 512) to (N, 512, 512) msk = msk.to(torch.long).squeeze(1) msk_pred = model(img) # shape (N, 3, 512, 512) # label_weights is determined according the liver_ratio & tumor_ratio # loss = CrossEntropyLoss(msk_pred, msk, label_weights=[1., 10., 100.], device=device) loss = DiceLoss(msk_pred, msk, label_weights=[1., 20., 50.], device=device) # loss = DiceLoss(msk_pred, msk, label_weights=[1., 20., 500.], device=device) if valid_mode: pass else: loss.backward() optimizer.step() loss = loss.item() # release cum_loss += loss if valid_mode: print("\r--------(valid) {:.2%} Loss: {:.3f} (time: {:.1f}s) |supp: {}".format( (iter_ind+1)/n_batch_per_epoch, cum_loss/(iter_ind+1), time.time()-st, supplement_logs), end="") else: print("\rEpoch: {:3}/{} {:.2%} Loss: {:.3f} (time: {:.1f}s) |supp: {}".format( (epoch+1), args.epochs, (iter_ind+1)/n_batch_per_epoch, cum_loss/(iter_ind+1), time.time()-st, supplement_logs), end="") print() if valid_mode: torch.set_grad_enabled(prev_grad_mode) valid_mean_loss = cum_loss/(iter_ind+1) # validation (mean) loss of the current epoch if best_model_path and (valid_mean_loss < best_valid_loss): printGreen("Valid loss decreases from {:.5f} to {:.5f}, saving best model.".format( best_valid_loss, valid_mean_loss)) best_valid_loss = valid_mean_loss # Only need to save the weights # torch.save(model.state_dict(), best_model_path) # save the entire model torch.save(model, best_model_path) return best_valid_loss
transform=T.ToTensor(), remove_alpha=True) """ ----------------- ----- Model ----- ----------------- """ s = UNet(3, 2) t = UNet(3, 3) if use_gpu: s.cuda() t.cuda() # lr = 0.001 seems to work WITHOUT PRETRAINING s_optim = optim.Adam(s.parameters(), lr=0.1) t_optim = optim.Adam(t.parameters(), lr=0.1) s_scheduler = torch.optim.lr_scheduler.StepLR(s_optim, step_size=10) t_scheduler = torch.optim.lr_scheduler.StepLR(t_optim, step_size=10) gan = GANv2(s=s, s_optim=s_optim, s_loss=CrossEntropyLoss2d().cuda(), s_scheduler=s_scheduler, g=t, g_optim=t_optim) gan.train_segmenter(train_dataset, n_epochs=20, n_batch=4, use_gpu=use_gpu,
# Число эпох N_EPOCHS = 10 # tensorboard writer = SummaryWriter(log_dir='./{}'.format(MODEL_NAME), comment=MODEL_NAME) # Задаем модель model = UNet(3, NUM_PTS) model.to(device) with torch.no_grad(): # writer.add_graph(model, next(iter(val_dataloader))['image'].to(device)) summary(model, next(iter(train_dataloader))['image'].shape[1:]) # Задаем параметры оптимизации optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE, amsgrad=True) # criterion = F.mse_loss criterion = AdaptiveWingLoss() # Временные параметры для выбора наилучшего результата best_val_loss, best_model_state_dict = np.inf, {} CURRENT_EPOCH = 0 for epoch in range(CURRENT_EPOCH, N_EPOCHS): train_loss = train_iter(MODEL_NAME, 470, epoch, model, train_dataloader, criterion,
def denoising(noise_im, clean_im, LR=1e-2, sigma=5, rho=1, eta=0.5, alpha=1, total_step=20, prob1_iter=500, noise_level=None, result_root=None, f=None): input_depth = 1 latent_dim = 1 en_net = UNet(input_depth, latent_dim, need_sigmoid=False).cuda() de_net = UNet(latent_dim, input_depth, need_sigmoid=False).cuda() parameters = [p for p in en_net.parameters() ] + [p for p in de_net.parameters()] optimizer = torch.optim.Adam(parameters, lr=LR) l2_loss = torch.nn.MSELoss().cuda() i0 = torch.Tensor(noise_im)[None, None, ...].cuda() noise_im_torch = torch.Tensor(noise_im)[None, None, ...].cuda() Y = torch.zeros_like(noise_im_torch).cuda() i0_til_torch = torch.Tensor(noise_im)[None, None, ...].cuda() diff_original_np = noise_im.astype(np.float32) - clean_im.astype( np.float32) diff_original_name = 'Original_dis.png' save_hist(diff_original_np, result_root + diff_original_name) best_psnr = 0 best_ssim = 0 for i in range(total_step): ################################# sub-problem 1 ############################### for i_1 in range(prob1_iter): mean_i = en_net(noise_im_torch) eps = mean_i.clone().normal_() out = de_net(mean_i + eps) total_loss = 0.5 * l2_loss(out, noise_im_torch) total_loss += 1 / (2 * sigma**2) * l2_loss(mean_i, i0) total_loss += (rho / 2) * l2_loss(i0 + Y, i0_til_torch) optimizer.zero_grad() total_loss.backward() optimizer.step() with torch.no_grad(): i0 = ((1 / sigma**2) * mean_i + rho * (i0_til_torch - Y) + alpha * noise_im_torch) / ( (1 / sigma**2) + rho + alpha) with torch.no_grad(): ################################# sub-problem 2 ############################### i0_np = i0.cpu().squeeze().detach().numpy() Y_np = Y.cpu().squeeze().detach().numpy() sig = noise_level i0_til_np = denoise_nl_means(i0_np + Y_np, h=20 * sig, sigma=sig, fast_mode=False, **patch_kw) i0_til_torch = torch.Tensor(i0_til_np[None, None, ...]).cuda() ################################# sub-problem 3 ############################### Y = Y + eta * (i0 - i0_til_torch) ############################################################################### denoise_obj_pil = Image.fromarray( (i0_np + Y_np).clip(0, 255).astype(np.uint8)) Y_np = Y.cpu().squeeze().detach().numpy() Y_norm_np = np.abs(Y_np) i0_pil = Image.fromarray(np.uint8(i0_np.clip(0, 255))) mean_i_np = mean_i.cpu().squeeze().detach().numpy().clip(0, 255) mean_i_pil = Image.fromarray(mean_i_np.astype(np.uint8)) out_np = out.cpu().squeeze().detach().numpy().clip(0, 255) out_pil = Image.fromarray(out_np.astype(np.uint8)) diff_np = mean_i_np - clean_im denoise_obj_name = 'denoise_obj_{:04d}'.format(i) + '.png' Y_name = 'Y_{:04d}'.format(i) + '.png' i0_name = 'i0_num_epoch_{:04d}'.format(i) + '.png' mean_i_name = 'Latent_im_num_epoch_{:04d}'.format(i) + '.png' out_name = 'res_of_dec_num_epoch_{:04d}'.format(i) + '.png' diff_name = 'Latent_dis_num_epoch_{:04d}'.format(i) + '.png' denoise_obj_pil.save(result_root + denoise_obj_name) save_heatmap(Y_norm_np, result_root + Y_name) i0_pil.save(result_root + i0_name) mean_i_pil.save(result_root + mean_i_name) out_pil.save(result_root + out_name) save_hist(diff_np, result_root + diff_name) i0_til_np = i0_til_torch.cpu().squeeze().detach().numpy() i0_til_np = np.clip(i0_til_np, 0, 255) psnr = compare_psnr(clean_im, i0_til_np, data_range=255) ssim = compare_ssim(clean_im, i0_til_np, data_range=255) i0_til_pil = Image.fromarray(i0_til_np.astype(np.uint8)) i0_til_pil.save(os.path.join(result_root, '{}'.format(i) + '.png')) print('Iteration: {:02d}, VAE Loss: {:f}, PSNR: {:f}, SSIM: {:f}'. format(i, total_loss.item(), psnr, ssim), file=f, flush=True) if best_psnr < psnr: best_psnr = psnr best_ssim = ssim else: break return i0_til_np, best_psnr, best_ssim
def train(args): ''' -------------------------Hyperparameters-------------------------- ''' EPOCHS = args.epochs START = 0 # could enter a checkpoint start epoch ITER = args.iterations # per epoch LR = args.lr MOM = args.momentum # LOGInterval = args.log_interval BATCHSIZE = args.batch_size TEST_BATCHSIZE = args.test_batch_size NUMBER_OF_WORKERS = args.workers DATA_FOLDER = args.data TESTSET_FOLDER = args.testset ROOT = args.run WEIGHT_DIR = os.path.join(ROOT, "weights") CUSTOM_LOG_DIR = os.path.join(ROOT, "additionalLOGS") CHECKPOINT = os.path.join(WEIGHT_DIR, str(args.model) + str(args.name) + ".pt") useTensorboard = args.tb # check existance of data if not os.path.isdir(DATA_FOLDER): print("data folder not existant or in wrong layout.\n\t", DATA_FOLDER) exit(0) # check existance of testset if TESTSET_FOLDER is not None and not os.path.isdir(TESTSET_FOLDER): print("testset folder not existant or in wrong layout.\n\t", DATA_FOLDER) exit(0) ''' ---------------------------preparations--------------------------- ''' # CUDA for PyTorch use_cuda = torch.cuda.is_available() device = torch.device("cuda:0" if use_cuda else "cpu") print("using device: ", str(device)) # loading the validation samples to make online evaluations path_to_valX = args.valX path_to_valY = args.valY valX = None valY = None if path_to_valX is not None and path_to_valY is not None \ and os.path.exists(path_to_valX) and os.path.exists(path_to_valY) \ and os.path.isfile(path_to_valX) and os.path.isfile(path_to_valY): with torch.no_grad(): valX, valY = torch.load(path_to_valX, map_location='cpu'), \ torch.load(path_to_valY, map_location='cpu') ''' ---------------------------loading dataset and normalizing--------------------------- ''' # Dataloader Parameters train_params = { 'batch_size': BATCHSIZE, 'shuffle': True, 'num_workers': NUMBER_OF_WORKERS } test_params = { 'batch_size': TEST_BATCHSIZE, 'shuffle': False, 'num_workers': NUMBER_OF_WORKERS } # create a folder for the weights and custom logs if not os.path.isdir(WEIGHT_DIR): os.makedirs(WEIGHT_DIR) if not os.path.isdir(CUSTOM_LOG_DIR): os.makedirs(CUSTOM_LOG_DIR) labelsNorm = None # NORMLABEL # normalizing on a trainingset wide mean and std mean = None std = None if args.norm: print('computing mean and std over trainingset') # computes mean and std over all ground truths in dataset to tackle the problem of numerical insignificance mean, std = computeMeanStdOverDataset('CONRADataset', DATA_FOLDER, train_params, device) print('\niodine (mean/std): {}\t{}'.format(mean[0], std[0])) print('water (mean/std): {}\t{}\n'.format(mean[1], std[1])) labelsNorm = transforms.Normalize(mean=[0, 0], std=std) m2, s2 = computeMeanStdOverDataset('CONRADataset', DATA_FOLDER, train_params, device, transform=labelsNorm) print("new mean and std are:") print('\nnew iodine (mean/std): {}\t{}'.format(m2[0], s2[0])) print('new water (mean/std): {}\t{}\n'.format(m2[1], s2[1])) traindata = CONRADataset(DATA_FOLDER, True, device=device, precompute=True, transform=labelsNorm) testdata = None if TESTSET_FOLDER is not None: testdata = CONRADataset(TESTSET_FOLDER, False, device=device, precompute=True, transform=labelsNorm) else: testdata = CONRADataset(DATA_FOLDER, False, device=device, precompute=True, transform=labelsNorm) trainingset = DataLoader(traindata, **train_params) testset = DataLoader(testdata, **test_params) ''' ----------------loading model and checkpoints--------------------- ''' if args.model == "unet": m = UNet(2, 2).to(device) print( "using the U-Net architecture with {} trainable params; Good Luck!" .format(count_trainables(m))) else: m = simpleConvNet(2, 2).to(device) o = optim.SGD(m.parameters(), lr=LR, momentum=MOM) loss_fn = nn.MSELoss() test_loss = None train_loss = None if len(os.listdir(WEIGHT_DIR)) != 0: checkpoints = os.listdir(WEIGHT_DIR) checkDir = {} latestCheckpoint = 0 for i, checkpoint in enumerate(checkpoints): stepOfCheckpoint = int( checkpoint.split(str(args.model) + str(args.name))[-1].split('.pt')[0]) checkDir[stepOfCheckpoint] = checkpoint latestCheckpoint = max(latestCheckpoint, stepOfCheckpoint) print("[{}] {}".format(stepOfCheckpoint, checkpoint)) # if on development machine, prompt for input, else just take the most recent one if 'faui' in os.uname()[1]: toUse = int(input("select checkpoint to use: ")) else: toUse = latestCheckpoint checkpoint = torch.load(os.path.join(WEIGHT_DIR, checkDir[toUse])) m.load_state_dict(checkpoint['model_state_dict']) m.to(device) # pushing weights to gpu o.load_state_dict(checkpoint['optimizer_state_dict']) train_loss = checkpoint['train_loss'] test_loss = checkpoint['test_loss'] START = checkpoint['epoch'] print("using checkpoint {}:\n\tloss(train/test): {}/{}".format( toUse, train_loss, test_loss)) else: print("starting from scratch") ''' -----------------------------training----------------------------- ''' global_step = 0 # calculating initial loss if test_loss is None or train_loss is None: print("calculating initial loss") m.eval() print("testset...") test_loss = calculate_loss(set=testset, loss_fn=loss_fn, length_set=len(testdata), dev=device, model=m) print("trainset...") train_loss = calculate_loss(set=trainingset, loss_fn=loss_fn, length_set=len(traindata), dev=device, model=m) ## SSIM and R value R = [] SSIM = [] performanceFLE = os.path.join(CUSTOM_LOG_DIR, "performance.csv") with open(performanceFLE, 'w+') as f: f.write( "step, SSIMiodine, SSIMwater, Riodine, Rwater, train_loss, test_loss\n" ) print("computing ssim and r coefficents to: {}".format(performanceFLE)) # printing runtime information print( "starting training at {} for {} epochs {} iterations each\n\t{} total". format(START, EPOCHS, ITER, EPOCHS * ITER)) print("\tbatchsize: {}\n\tloss: {}\n\twill save results to \"{}\"".format( BATCHSIZE, train_loss, CHECKPOINT)) print( "\tmodel: {}\n\tlearningrate: {}\n\tmomentum: {}\n\tnorming output space: {}" .format(args.model, LR, MOM, args.norm)) #start actual training loops for e in range(START, START + EPOCHS): # iterations will not be interupted with validation and metrics for i in range(ITER): global_step = (e * ITER) + i # training m.train() iteration_loss = 0 for x, y in tqdm(trainingset): x, y = x.to(device=device, dtype=torch.float), y.to(device=device, dtype=torch.float) pred = m(x) loss = loss_fn(pred, y) iteration_loss += loss.item() o.zero_grad() loss.backward() o.step() print("\niteration {}: --accumulated loss {}".format( global_step, iteration_loss)) # validation, saving and logging print("\nvalidating") m.eval() # disable dropout batchnorm etc print("testset...") test_loss = calculate_loss(set=testset, loss_fn=loss_fn, length_set=len(testdata), dev=device, model=m) print("trainset...") train_loss = calculate_loss(set=trainingset, loss_fn=loss_fn, length_set=len(traindata), dev=device, model=m) print("calculating SSIM and R coefficients") currSSIM, currR = performance(set=testset, dev=device, model=m, bs=TEST_BATCHSIZE) print("SSIM (iod/water): {}/{}\nR (iod/water): {}/{}".format( currSSIM[0], currSSIM[1], currR[0], currR[1])) with open(performanceFLE, 'a') as f: newCSVline = "{}, {}, {}, {}, {}, {}, {}\n".format( global_step, currSSIM[0], currSSIM[1], currR[0], currR[1], train_loss, test_loss) f.write(newCSVline) print("wrote new line to csv:\n\t{}".format(newCSVline)) ''' if valX and valY were set in preparations, use them to perform analytics. if not, use the first sample from the testset to perform analytics ''' with torch.no_grad(): truth, pred = None, None IMAGE_LOG_DIR = os.path.join(CUSTOM_LOG_DIR, str(global_step)) if not os.path.isdir(IMAGE_LOG_DIR): os.makedirs(IMAGE_LOG_DIR) if valX is not None and valY is not None: batched = np.zeros((BATCHSIZE, *valX.numpy().shape)) batched[0] = valX.numpy() batched = torch.from_numpy(batched).to(device=device, dtype=torch.float) pred = m(batched) pred = pred.cpu().numpy()[0] truth = valY.numpy() # still on cpu assert pred.shape == truth.shape else: for x, y in testset: # x, y in shape[2,2,480,620] [b,c,h,w] x, y = x.to(device=device, dtype=torch.float), y.to(device=device, dtype=torch.float) pred = m(x) pred = pred.cpu().numpy()[ 0] # taking only the first sample of batch truth = y.cpu().numpy()[ 0] # first projection for evaluation advanvedMetrics(truth, pred, mean, std, global_step, args.norm, IMAGE_LOG_DIR) print("logging") CHECKPOINT = os.path.join( WEIGHT_DIR, str(args.model) + str(args.name) + str(global_step) + ".pt") torch.save( { 'epoch': e + 1, # end of this epoch; so resume at next. 'model_state_dict': m.state_dict(), 'optimizer_state_dict': o.state_dict(), 'train_loss': train_loss, 'test_loss': test_loss }, CHECKPOINT) print('\tsaved weigths to: ', CHECKPOINT) if logger is not None and train_loss is not None: logger.add_scalar('test_loss', test_loss, global_step=global_step) logger.add_scalar('train_loss', train_loss, global_step=global_step) logger.add_image("iodine-prediction", pred[0].reshape(1, 480, 620), global_step=global_step) logger.add_image("water-prediction", pred[1].reshape(1, 480, 620), global_step=global_step) # logger.add_image("water-prediction", wat) print( "\ttensorboard updated with test/train loss and a sample image" ) elif train_loss is not None: print("\tloss of global-step {}: {}".format( global_step, train_loss)) elif not useTensorboard: print("\t(tb-logging disabled) test/train loss: {}/{} ".format( test_loss, train_loss)) else: print("\tno loss accumulated yet") # saving final results print("saving upon exit") torch.save( { 'epoch': EPOCHS, 'model_state_dict': m.state_dict(), 'optimizer_state_dict': o.state_dict(), 'train_loss': train_loss, 'test_loss': test_loss }, CHECKPOINT) print('\tsaved progress to: ', CHECKPOINT) if logger is not None and train_loss is not None: logger.add_scalar('test_loss', test_loss, global_step=global_step) logger.add_scalar('train_loss', train_loss, global_step=global_step)