def main(): global best_test_bpd last_checkpoints = [] lipschitz_constants = [] ords = [] # if args.resume: # validate(args.begin_epoch - 1, model, ema) #liveloss = PlotLosses() #liveloss = PlotLosses() liveloss = PlotLosses() for epoch in range(args.begin_epoch, args.nepochs): logs = {} logger.info('Current LR {}'.format(optimizer.param_groups[0]['lr'])) running_loss = train(epoch, model) #train(epoch, model) lipschitz_constants.append(get_lipschitz_constants(model)) #ords.append(get_ords(model)) #ords.append(get_ords(model)) ords.append(get_ords(model)) logger.info('Lipsh: {}'.format(pretty_repr(lipschitz_constants[-1]))) logger.info('Order: {}'.format(pretty_repr(ords[-1]))) #epoch_loss = running_loss / len(dataloaders[phase].dataset) epoch_loss = running_loss / len( datasets.CIFAR10( args.dataroot, train=True, transform=transform_train)) logs['log loss'] = epoch_loss.item() liveloss.update(logs) liveloss.draw() if args.ema_val: test_bpd = validate(epoch, model, ema) else: test_bpd = validate(epoch, model) if args.scheduler and scheduler is not None: scheduler.step() if test_bpd < best_test_bpd: best_test_bpd = test_bpd utils.save_checkpoint( { 'state_dict': model.state_dict(), 'optimizer_state_dict': optimizer.state_dict(), 'args': args, 'ema': ema, 'test_bpd': test_bpd, }, os.path.join(args.save, 'moMoModels'), epoch, last_checkpoints, num_checkpoints=5) """ utils.save_checkpoint({ 'state_dict': model.state_dict(), 'optimizer_state_dict': optimizer.state_dict(), 'args': args, 'ema': ema, 'test_bpd': test_bpd, }, os.path.join(args.save, 'mMoModels'), epoch, last_checkpoints, num_checkpoints=5) utils.save_checkpoint({ 'state_dict': model.state_dict(), 'optimizer_state_dict': optimizer.state_dict(), 'args': args, 'ema': ema, 'test_bpd': test_bpd, }, os.path.join(args.save, 'mModels'), epoch, last_checkpoints, num_checkpoints=5) utils.save_checkpoint({ 'state_dict': model.state_dict(), 'optimizer_state_dict': optimizer.state_dict(), 'args': args, 'ema': ema, 'test_bpd': test_bpd, }, os.path.join(args.save, 'models'), epoch, last_checkpoints, num_checkpoints=5) """ torch.save( { 'state_dict': model.state_dict(), 'optimizer_state_dict': optimizer.state_dict(), 'args': args, 'ema': ema, 'test_bpd': test_bpd, }, os.path.join(args.save, 'models', '010mmoosttMoosttRecentt.pth')) """
init_layer = layers.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)) else: transform_train = transforms.Compose([ transforms.Resize(args.imagesize), transforms.RandomHorizontalFlip(), transforms.ToTensor(), add_noise, ]) transform_test = transforms.Compose([ transforms.Resize(args.imagesize), transforms.ToTensor(), add_noise, ]) init_layer = layers.LogitTransform(0.05) train_loader = torch.utils.data.DataLoader( datasets.CIFAR10(args.dataroot, train=True, transform=transform_train), batch_size=args.batchsize, shuffle=True, num_workers=args.nworkers, ) test_loader = torch.utils.data.DataLoader( datasets.CIFAR10(args.dataroot, train=False, transform=transform_test), batch_size=args.val_batchsize, shuffle=False, num_workers=args.nworkers, ) elif args.data == 'mnist': im_dim = 1 init_layer = layers.LogitTransform(1e-6) n_classes = 10 train_loader = torch.utils.data.DataLoader(
transform_test = transforms.Compose([ transforms.Resize(args.imagesize), transforms.ToTensor(), add_noise, ]) init_layer = layers.LogitTransform(0.05) #train_loader = torch.utils.data.DataLoader( # datasets.CIFAR10(args.dataroot, train=True, transform=transform_train), # batch_size=args.batchsize, # shuffle=True, # num_workers=args.nworkers, #) train_trainLoader = datasets.CIFAR10(args.dataroot, train=True, transform=transform_train) train_trainLoader2 = datasets.CIFAR10(args.dataroot, train=True, transform=transform_train) idx = torch.tensor(train_trainLoader.cifar10.train_labels) == 0 train_trainLoader = torch.utils.data.dataset.Subset( train_trainLoader, np.where(idx == 1)[0]) # train_trainLoader.mnist.train_data = train_trainLoader.mnist.train_data[idx] evens = list(range(0, len(train_trainLoader), 2)) train_trainLoader = torch.utils.data.Subset(train_trainLoader, evens) evens = list(range(0, len(train_trainLoader), 2))