def getSEMEION(batch_size, test_batch_size, img_size, **kwargs): num_workers = kwargs.setdefault('num_workers', 0) kwargs.pop('input_size', None) print("Building SEMEION data loader with {} workers".format(num_workers)) ds = [] if 'apply_grayscale' in kwargs: del kwargs['apply_grayscale'] train_loader = DataLoader( datasets.SEMEION( root='data/semeion', download=True, transform=transforms.Compose([ transforms.Resize(img_size), transforms.ToTensor(), ])), batch_size=batch_size, shuffle=True, drop_last=True, **kwargs) ds.append(train_loader) test_loader = DataLoader( datasets.SEMEION( root='data/semeion', download=True, transform=transforms.Compose([ transforms.Resize(img_size), transforms.ToTensor(), ])), batch_size=batch_size, shuffle=False, drop_last=True, **kwargs) ds.append(test_loader) return ds
def semeion(): return collect_download_configs( lambda: datasets.SEMEION(ROOT, download=True), name="SEMEION", file="semeion", )
def main(): # Training settings parser = argparse.ArgumentParser(description='Amortized approximation on MNIST') parser.add_argument('--batch-size', type=int, default=256, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=64, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--approx-epochs', type=int, default=200, metavar='N', help='number of epochs to approx (default: 10)') parser.add_argument('--lr', type=float, default=1e-2, metavar='LR', help='learning rate (default: 0.0005)') parser.add_argument('--momentum', type=float, default=0.5, metavar='M', help='SGD momentum (default: 0.5)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') parser.add_argument('--S', type=int, default=100, metavar='N', help='number of posterior samples from the Bayesian model') parser.add_argument('--model-path', type=str, default='../saved_models/mnist_sgld/', metavar='N', help='number of posterior samples from the Bayesian model') parser.add_argument('--from-approx-model', type=int, default=1, metavar='N', help='if our model is loaded or trained') parser.add_argument('--test-ood-from-disk', type=int, default=1, help='generate test samples or load from disk') parser.add_argument('--ood-name', type=str, default='omniglot', help='name of the used ood dataset') args = parser.parse_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'num_workers': 8, 'pin_memory': True} if use_cuda else {} tr_data = MNIST('../data', train=True, transform=transforms.Compose([ transforms.Resize((28, 28)), transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]), download=True) te_data = MNIST('../data', train=False, transform=transforms.Compose([ transforms.Resize((28, 28)), transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]), download=True) train_loader = torch.utils.data.DataLoader( tr_data, batch_size=args.batch_size, shuffle=False, **kwargs) test_loader = torch.utils.data.DataLoader( te_data, batch_size=args.batch_size, shuffle=False, **kwargs) if args.ood_name == 'omniglot': ood_data = datasets.Omniglot('../../data', download=True, transform=transforms.Compose([ transforms.Resize((28, 28)), transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)), ])) elif args.ood_name == 'SEMEION': ood_data = datasets.SEMEION('../../data', download=True, transform=transforms.Compose([ transforms.Resize((28, 28)), transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)), ])) ood_loader = torch.utils.data.DataLoader( ood_data, batch_size=args.batch_size, shuffle=False, **kwargs) model = mnist_mlp(dropout=False).to(device) model.load_state_dict(torch.load(args.model_path + 'sgld-mnist.pt')) test(args, model, device, test_loader) if args.from_approx_model == 0: output_samples = torch.load(args.model_path + 'mnist-sgld-train-samples.pt') # --------------- training approx --------- fmodel = mnist_mlp_h().to(device) gmodel = mnist_mlp_g().to(device) if args.from_approx_model == 0: g_optimizer = optim.SGD(gmodel.parameters(), lr=args.lr) f_optimizer = optim.SGD(fmodel.parameters(), lr=args.lr) best_acc = 0 for epoch in range(1, args.approx_epochs + 1): train_approx(args, fmodel, gmodel, device, train_loader, f_optimizer, g_optimizer, output_samples, epoch) acc = test(args, fmodel, device, test_loader) # if (args.save_approx_model == 1): if acc > best_acc: torch.save(fmodel.state_dict(), args.model_path + 'sgld-mnist-mmd-mean.pt') torch.save(gmodel.state_dict(), args.model_path + 'sgld-mnist-mmd-conc.pt') best_acc = acc else: fmodel.load_state_dict(torch.load(args.model_path + 'sgld-mnist-mmd-mean.pt')) gmodel.load_state_dict(torch.load(args.model_path + 'sgld-mnist-mmd-conc.pt')) print('generating teacher particles for testing&ood data ...') # generate particles for test and ood dataset model.train() if args.test_ood_from_disk == 1: teacher_test_samples = torch.load(args.model_path + 'mnist-sgld-test-samples.pt') else: with torch.no_grad(): # obtain ensemble outputs all_samples = [] for i in range(500): samples_a_round = [] for data, target in test_loader: data = data.to(device) data = data.view(data.shape[0], -1) output = F.softmax(model(data)) samples_a_round.append(output) samples_a_round = torch.cat(samples_a_round).cpu() all_samples.append(samples_a_round) all_samples = torch.stack(all_samples).permute(1,0,2) torch.save(all_samples, args.model_path + 'mnist-sgld-test-samples.pt') teacher_test_samples = all_samples if args.test_ood_from_disk == 1: teacher_ood_samples = torch.load(args.model_path + 'mnist-sgld-' + args.ood_name + '-samples.pt') else: with torch.no_grad(): # obtain ensemble outputs all_samples = [] for i in range(500): samples_a_round = [] for data, target in ood_loader: data = data.to(device) data = data.view(data.shape[0], -1) output = F.softmax(model(data)) samples_a_round.append(output) samples_a_round = torch.cat(samples_a_round).cpu() all_samples.append(samples_a_round) all_samples = torch.stack(all_samples).permute(1,0,2) torch.save(all_samples, args.model_path + 'mnist-sgld-' + args.ood_name + '-samples.pt') teacher_ood_samples = all_samples eval_approx(args, fmodel, gmodel, device, test_loader, ood_loader, teacher_test_samples, teacher_ood_samples)
def main(): # Training settings parser = argparse.ArgumentParser( description='run approximation to LeNet on Mnist') parser.add_argument('--batch-size', type=int, default=256, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=100, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=10, metavar='N', help='number of epochs to train (default: 10)') parser.add_argument('--lr', type=float, default=0.001, metavar='LR', help='learning rate (default: 0.0005)') parser.add_argument('--momentum', type=float, default=0.5, metavar='M', help='SGD momentum (default: 0.5)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument( '--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') parser.add_argument('--dropout-rate', type=float, default=0.5, metavar='p_drop', help='dropout rate') parser.add_argument( '--S', type=int, default=500, metavar='N', help='number of posterior samples from the Bayesian model') parser.add_argument( '--model-path', type=str, default='../saved_models/mnist_sgld/', metavar='N', help='number of posterior samples from the Bayesian model') args = parser.parse_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) device = torch.device("cuda" if use_cuda else "cpu") kwargs = {'num_workers': 8, 'pin_memory': True} if use_cuda else {} train_loader = torch.utils.data.DataLoader(datasets.MNIST( '../data', train=True, download=True, transform=transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, ), (0.5, ))])), batch_size=args.batch_size, shuffle=True, **kwargs) test_loader = torch.utils.data.DataLoader(datasets.MNIST( '../data', train=False, transform=transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, ), (0.5, ))])), batch_size=args.test_batch_size, shuffle=False, **kwargs) model = mnist_mlp(dropout=False).to(device) optimizer = SGLD(model.parameters(), lr=args.lr) import copy import pickle as pkl for epoch in range(1, args.epochs + 1): train_bayesian(args, model, device, train_loader, optimizer, epoch) print("epoch: {}".format(epoch)) test(args, model, device, test_loader) # save models torch.save(model.state_dict(), args.model_path + 'sgld-mnist.pt') # save samples param_samples = [] while (1): for idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() data = data.view(data.shape[0], -1) output = model(data) loss = F.nll_loss(F.log_softmax(output, dim=1), target) loss.backward() optimizer.step() param_samples.append(copy.deepcopy(model.state_dict())) if param_samples.__len__() >= args.S: print('1', len(param_samples)) break if param_samples.__len__() >= args.S: print('2', len(param_samples)) break with open(args.model_path + "sgld_samples.pkl", "wb") as f: print('3', len(param_samples)) pkl.dump(param_samples, f) test(args, model, device, test_loader) train_loader = torch.utils.data.DataLoader(datasets.MNIST( '../data', train=True, download=True, transform=transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, ), (0.5, ))])), batch_size=args.batch_size, shuffle=False, **kwargs) test_loader = torch.utils.data.DataLoader(datasets.MNIST( '../data', train=False, transform=transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, ), (0.5, ))])), batch_size=args.test_batch_size, shuffle=False, **kwargs) # generate teacher train samples with torch.no_grad(): # obtain ensemble outputs all_samples = [] for i in range(500): samples_a_round = [] model.load_state_dict(param_samples[i]) for data, target in train_loader: data = data.to(device) data = data.view(data.shape[0], -1) output = F.softmax(model(data)) samples_a_round.append(output) samples_a_round = torch.cat(samples_a_round).cpu() all_samples.append(samples_a_round) all_samples = torch.stack(all_samples).permute(1, 0, 2) torch.save(all_samples, args.model_path + 'mnist-sgld-train-samples.pt') # generate teacher test samples with torch.no_grad(): # obtain ensemble outputs all_samples = [] for i in range(500): samples_a_round = [] model.load_state_dict(param_samples[i]) for data, target in test_loader: data = data.to(device) data = data.view(data.shape[0], -1) output = F.softmax(model(data)) samples_a_round.append(output) samples_a_round = torch.cat(samples_a_round).cpu() all_samples.append(samples_a_round) all_samples = torch.stack(all_samples).permute(1, 0, 2) torch.save(all_samples, args.model_path + 'mnist-sgld-test-samples.pt') # generate teacher omniglot samples ood_data = datasets.Omniglot( '../../data', download=True, transform=transforms.Compose([ # transforms.ToPILImage(), transforms.Resize((28, 28)), transforms.ToTensor(), transforms.Normalize((0.5, ), (0.5, )), ])) ood_loader = torch.utils.data.DataLoader(ood_data, batch_size=args.batch_size, shuffle=False, **kwargs) with torch.no_grad(): # obtain ensemble outputs all_samples = [] for i in range(500): samples_a_round = [] model.load_state_dict(param_samples[i]) for data, target in ood_loader: data = data.to(device) data = data.view(data.shape[0], -1) output = F.softmax(model(data)) samples_a_round.append(output) samples_a_round = torch.cat(samples_a_round).cpu() all_samples.append(samples_a_round) all_samples = torch.stack(all_samples).permute(1, 0, 2) torch.save(all_samples, args.model_path + 'mnist-sgld-omniglot-samples.pt') # generate teacher SEMEION samples ood_data = datasets.SEMEION( '../../data', download=True, transform=transforms.Compose([ # transforms.ToPILImage(), transforms.Resize((28, 28)), transforms.ToTensor(), transforms.Normalize((0.5, ), (0.5, )), ])) ood_loader = torch.utils.data.DataLoader(ood_data, batch_size=args.batch_size, shuffle=False, **kwargs) with torch.no_grad(): # obtain ensemble outputs all_samples = [] for i in range(500): samples_a_round = [] model.load_state_dict(param_samples[i]) for data, target in ood_loader: data = data.to(device) data = data.view(data.shape[0], -1) output = F.softmax(model(data)) samples_a_round.append(output) samples_a_round = torch.cat(samples_a_round).cpu() all_samples.append(samples_a_round) all_samples = torch.stack(all_samples).permute(1, 0, 2) torch.save(all_samples, args.model_path + 'mnist-sgld-SEMEION-samples.pt')
split="train", download=True) svhn_test = datasets.SVHN(root=os.path.join(dataset_path, "svhn"), split="test", download=True) svhn_extra = datasets.SVHN(root=os.path.join(dataset_path, "svhn"), split="extra", download=True) print("svhn数据集,train data shape:", svhn_train.data.shape) print("svhn数据集,train label shape:", svhn_train.labels.shape) print("svhn数据集,test data shape:", svhn_test.data.shape) print("svhn数据集,test label shape:", svhn_test.data.shape) print("svhn数据集,extra data shape:", svhn_extra.data.shape) print("svhn数据集,extra label shape:", svhn_extra.data.shape) '''Semeion手写字体识别数据集''' semeion = datasets.SEMEION(root=os.path.join(dataset_path, "semeion"), download=True) print("Semeion数据集,data shape:", semeion.data.shape) print("Semeion数据集,labels shape:", semeion.labels.shape) '''STL10图像分类数据集''' '''STL10包括500个训练数据、100000个未标注数据,图像是96X96像素,共10类:airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck''' stl10_train = datasets.STL10(root=os.path.join(dataset_path, "stl10"), split="train", download=True) stl10_test = datasets.STL10(root=os.path.join(dataset_path, "stl10"), split="test", download=True) stl10_unlabeled = datasets.STL10(root=os.path.join(dataset_path, "stl10"), split="unlabeled", download=True) print("STL10数据集,train data shape:", stl10_train.data.shape) print("STL10数据集,train labels shape:", stl10_train.labels.shape)
elif args.dataset == 'semeion': nh = 16 nw = 16 nc = 1 num_class = 10 # the digits from 0 to 9 (written by 80 people twice) end_epoch = 50 if args.data_aug == 1: end_epoch = 200 train_transform = transforms.Compose([ transforms.RandomCrop(16, padding=1), transforms.RandomAffine(4, scale=(1.05, 1.05)), transforms.ToTensor() ]) test_transform = transforms.Compose([transforms.ToTensor()]) train_data = datasets.SEMEION(data_path, transform=train_transform, download=True) test_data = train_data random_index = np.load(data_path + '/random_index.npy') train_size = 1000 train_Sampler = SubsetRandomSampler(random_index[range(train_size)]) test_Sampler = SubsetRandomSampler(random_index[range( train_size, len(test_data))]) Shuffle = False elif args.dataset == 'fakedata': nh = 24 nw = 24 nc = 3 num_class = 10 end_epoch = 50 train_size = 1000