def load_model(self): if self.cuda: self.device = torch.device('cuda') cudnn.benchmark = True else: self.device = torch.device('cpu') self.model = LeNet().to(self.device) # self.model = AlexNet().to(self.device) # self.model = VGG11().to(self.device) # self.model = VGG13().to(self.device) # self.model = VGG16().to(self.device) # self.model = VGG19().to(self.device) # self.model = GoogLeNet().to(self.device) # self.model = resnet18().to(self.device) # self.model = resnet34().to(self.device) # self.model = resnet50().to(self.device) # self.model = resnet101().to(self.device) # self.model = resnet152().to(self.device) # self.model = DenseNet121().to(self.device) # self.model = DenseNet161().to(self.device) # self.model = DenseNet169().to(self.device) # self.model = DenseNet201().to(self.device) # self.model = WideResNet(depth=28, num_classes=10).to(self.device) self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr) self.scheduler = optim.lr_scheduler.MultiStepLR(self.optimizer, milestones=[75, 150], gamma=0.5) self.criterion = nn.CrossEntropyLoss().to(self.device)
def measure_computation_fraction_lenet(train_loader): """Measure percentage of computation time spent in each layer of LeNet. """ model = LeNet(n_channels=n_channels, size=32).to(device) loader = train_loader it = iter(loader) data, target = next(it) data, target = data.to(device), target.to(device)
def pt2stru(pt): if "lenet" in pt: return LeNet() elif "l0net" in pt: return L0Net(mean=1) elif "VGG" in pt: if "l0" in pt: return L0VGG(cifar10_network, loc=g_mean, temp=g_temp) else: return VGG(cifar10_network)
def test_lenet(self): from models import LeNet n_outputs = 10 model = LeNet(num_classes=n_outputs) model.eval() x = torch.randn(20,3,32,32) outputs = model(x) self.assertTrue(outputs.shape[0] == x.shape[0]) self.assertTrue(outputs.shape[1] == n_outputs)
def test_get_mods(self): from models import LeNet from altmin import get_mods model = LeNet() model.eval() x = torch.randn(20, 3, 32, 32) outputs = model(x) model_mods = get_mods(model) self.assertTrue( len(model.features) + len(model.classifier) >= len(model_mods))
def _load_model(self): self.model = LeNet() self.current_iteration = 0 if os.path.exists(self.args.model_path): try: print("Loading model from: {}".format(self.args.model_path)) self.model.load_state_dict(torch.load(self.args.model_path)) self.current_iteration = joblib.load("{}.iter".format( self.args.model_path)) except Exception as e: print( "Exception: {}\nCould not load model from {} - starting from scratch" .format(e, self.args.model_path))
def get_network(name, baseline=True, **kwargs): mean = kwargs.get("mean") temp = kwargs.get("temp") if name == "mnist": if baseline: return LeNet() else: try: return L0Net(mean=mean, temp=temp) except KeyError: print("No Key Named 'mean'") elif name == "cifar10": if baseline: return VGG(cifar10_network) else: return L0VGG(cifar10_network, loc=mean, temp=temp)
def get_model(name, device): """ Returns required classifier and autoencoder :param name: :return: Autoencoder, Classifier """ if name == 'lenet': model = LeNet(in_channels=channels).to(device) elif name == 'alexnet': model = AlexNet(channels=channels, num_classes=10).to(device) elif name == 'vgg': model = VGG(in_channels=channels, num_classes=10).to(device) autoencoder = CAE(in_channels=channels).to(device) return model, autoencoder
def test_get_codes(self): from models import LeNet from altmin import get_mods, get_codes model = LeNet() model.eval() x = torch.randn(20, 3, 32, 32) outputs = model(x) model_mods = get_mods(model) out1, codes = get_codes(model_mods, x) out2 = model_mods(x) self.assertAlmostEqual((outputs - out1).abs().mean().item(), 0) self.assertAlmostEqual((out1 - out2).abs().mean().item(), 0)
def alignment_lenet(augmentations): """Compute the kernel target alignment on LeNet. Since the feature map is initialized to be random and then trained, unlike kernels where feature map is fixed, kernel target alignment doesn't predict the accuracy at all. """ for augmentation in augmentations: print(augmentation.name) model_base = LeNet().to(device) optimizer = sgd_opt_from_model(model_base) # Train LeNet for 1 epoch first _ = train_all_epochs(train_loader, valid_loader, model_base, optimizer, 1) model = LeNetAug().to(device) model.load_state_dict(model_base.state_dict()) loader = loader_from_dataset(augmentation.dataset) print(kernel_target_alignment_augmented(loader, model))
def get_model(name, input_size=None, output=None): name = name.lower() if name == 'lenet-300-100': model = LeNet_300_100(input_size, output) elif name == 'lenet-5': model = LeNet(input_size, output) elif 'vgg' in name: # if 'bn' in name: if name == 'vgg11': model = vgg11(pretrained=False, num_classes=output) elif name == 'vgg16': model = vgg16(pretrained=False, num_classes=output) else: assert False for n, m in model.named_modules(): if hasattr(m, 'bias') and not isinstance(m, _BatchNorm): if m.bias is not None: if m.bias.sum() == 0: m.bias = None elif 'alexnet' in name: model = AlexNet(num_classes=output) for n, m in model.named_modules(): if hasattr(m, 'bias') and not isinstance(m, _BatchNorm): if m.bias is not None: if m.bias.sum() == 0: m.bias = None elif 'resnet' in name: if name == 'resnet20': model = resnet20(num_classes=output) elif name == 'resnet32': model = resnet32(num_classes=output) else: assert False for n, m in model.named_modules(): if hasattr(m, 'bias') and not isinstance(m, _BatchNorm): if m.bias is not None: if m.bias.sum() == 0: m.bias = None else: assert False return model
def main(config): logger = prepare_logger(config) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # get loaders if not config.is_train_source: target_loader = get_loader(type="MNIST", train=False, batch_size=config.batch_size) source_train_loader = get_loader(type="SVHN", train=True, batch_size=config.batch_size) source_test_loader = get_loader(type="SVHN", train=False, batch_size=config.batch_size) # build source classifier model_src = LeNet(config.num_gpus).to(device) if (not config.is_train_source) or config.is_finetune: model_src.load_state_dict(torch.load(config.model_dir)) # train source classifier if config.is_train_source: logger.info("train source classifier..") train_source(model_src, source_train_loader, source_test_loader, config, logger) logger.info("evaluate source classifier..") logger.info("test accurracy in source domain: %f\n" % (evaluate(model_src, source_test_loader))) else: # initialize target classifer with source classifer model_trg = torch.load(open("./pretrained/lenet-source.pth", "rb")) # build discriminator D = Discriminator(config.num_gpus) # adaptation process logger.info("start adaptation process..") adapt_target_domain(D, model_src, model_trg, source_train_loader, target_loader, config) logger.info("evaluate target classifier..") logger.info("accurracy in target domain: %f\n" % (evaluate(model_trg, target_loader)))
def main(): args = parse_args() paths = Paths() checkpoints_path = str(paths.CHECKPOINTS_PATH) logging_path = str(paths.LOG_PATH) callbacks = [PrintCallback()] checkpoint_callback = ModelCheckpoint(filepath=checkpoints_path + '/{epoch}-{val_acc:.3f}', save_top_k=True, verbose=True, monitor='val_acc', mode='max', prefix='') early_stop_callback = EarlyStopping(monitor='val_acc', mode='max', verbose=False, strict=False, min_delta=0.0, patience=2) gpus = gpu_count() log_save_interval = args.log_save_interval logger = TensorBoardLogger(save_dir=logging_path, name='tuna-log') logger.log_hyperparams(args) max_epochs = args.epochs model = LeNet(hparams=args, paths=paths) trainer = Trainer( callbacks=callbacks, checkpoint_callback=checkpoint_callback, early_stop_callback=early_stop_callback, fast_dev_run=True, gpus=gpus, log_save_interval=log_save_interval, logger=logger, max_epochs=max_epochs, min_epochs=1, show_progress_bar=True, weights_summary='full', ) trainer.fit(model)
torchvision.transforms.CenterCrop(28), # 从图片中间切出224*224的图片 torchvision.transforms.ToTensor(), # 将图片(Image)转成Tensor, 归一化至[0, 1] torchvision.transforms.Normalize(mean=[.5, .5, .5], std=[.5, .5, .5]) # 标准化至[-1, 1], 规定均值和标准差 ]) test_dataset = DOGCAT(root='../Pytorch-Tutorial/datasets/dogcat_2', train=False, transform=transform) test_loader = Data.DataLoader(dataset=test_dataset, batch_size=100, shuffle=False, num_workers=2) net = LeNet() print(net) if os.path.isfile('saves/dogcat_lenet_params.pkl'): net.load_state_dict(torch.load('saves/dogcat_lenet_params.pkl')) else: print("dogcat_lenet_params.pkl don't exists.") exit() # Test the Model total = 0 correct = 0 for images, labels in test_loader: images = Variable(images) outputs = net(images) _, predicted = torch.max(outputs.data, 1)
def model_gen_fun(): model = LeNet(num_classes=1, num_channels=1).eval() return model
def main(): if not os.path.isdir(CHECKPOINT): os.makedirs(CHECKPOINT) print('==> Preparing dataset') trainloader, testloader = load_CIFAR(batch_size=BATCH_SIZE, num_workers=NUM_WORKERS) CLASSES = [] AUROCs = [] auroc = AverageMeter() for t, cls in enumerate(ALL_CLASSES): print('\nTask: [%d | %d]\n' % (t + 1, len(ALL_CLASSES))) CLASSES = [cls] print("==> Creating model") model = LeNet(num_classes=1) if CUDA: model = model.cuda() model = nn.DataParallel(model) cudnn.benchmark = True print(' Total params: %.2fK' % (sum(p.numel() for p in model.parameters()) / 1000)) criterion = nn.BCELoss() optimizer = optim.SGD(model.parameters(), lr=LEARNING_RATE, momentum=MOMENTUM, weight_decay=WEIGHT_DECAY) print("==> Learning") best_loss = 1e10 learning_rate = LEARNING_RATE for epoch in range(EPOCHS): # decay learning rate if (epoch + 1) % EPOCHS_DROP == 0: learning_rate *= LR_DROP for param_group in optimizer.param_groups: param_group['lr'] = learning_rate print('Epoch: [%d | %d]' % (epoch + 1, EPOCHS)) train_loss = train(trainloader, model, criterion, CLASSES, CLASSES, optimizer=optimizer, use_cuda=CUDA) test_loss = train(testloader, model, criterion, CLASSES, CLASSES, test=True, use_cuda=CUDA) # save model is_best = test_loss < best_loss best_loss = min(test_loss, best_loss) save_checkpoint( { 'epoch': epoch + 1, 'state_dict': model.state_dict(), 'loss': test_loss, 'optimizer': optimizer.state_dict() }, CHECKPOINT, is_best) print("==> Calculating AUROC") filepath_best = os.path.join(CHECKPOINT, "best.pt") checkpoint = torch.load(filepath_best) model.load_state_dict(checkpoint['state_dict']) new_auroc = calc_avg_AUROC(model, testloader, CLASSES, CLASSES, CUDA) auroc.update(new_auroc) print('New Task AUROC: {}'.format(new_auroc)) print('Average AUROC: {}'.format(auroc.avg)) AUROCs.append(auroc.avg) print('\nAverage Per-task Performance over number of tasks') for i, p in enumerate(AUROCs): print("%d: %f" % (i + 1, p))
def main(): # Data Loader (Input Pipeline) print('loading dataset...') train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, num_workers=args.num_workers, drop_last=False, shuffle=False) val_loader = torch.utils.data.DataLoader(dataset=val_dataset, batch_size=batch_size, num_workers=args.num_workers, drop_last=False, shuffle=False) test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, num_workers=args.num_workers, drop_last=False, shuffle=False) # Define models print('building model...') if args.dataset == 'mnist': clf1 = LeNet() if args.dataset == 'fashionmnist': clf1 = resnet.ResNet18_F(10) if args.dataset == 'cifar10': clf1 = resnet.ResNet34(10) if args.dataset == 'svhn': clf1 = resnet.ResNet34(10) clf1.cuda() optimizer = torch.optim.SGD(clf1.parameters(), lr=args.lr, weight_decay=args.weight_decay) with open(txtfile, "a") as myfile: myfile.write('epoch train_acc val_acc test_acc\n') epoch = 0 train_acc = 0 val_acc = 0 # evaluate models with random weights test_acc = evaluate(test_loader, clf1) print('Epoch [%d/%d] Test Accuracy on the %s test data: Model1 %.4f %%' % (epoch + 1, args.n_epoch_1, len(test_dataset), test_acc)) # save results with open(txtfile, "a") as myfile: myfile.write( str(int(epoch)) + ' ' + str(train_acc) + ' ' + str(val_acc) + ' ' + str(test_acc) + ' ' + "\n") best_acc = 0.0 # training for epoch in range(1, args.n_epoch_1): # train models clf1.train() train_acc = train(clf1, train_loader, epoch, optimizer, nn.CrossEntropyLoss()) # validation val_acc = evaluate(val_loader, clf1) # evaluate models test_acc = evaluate(test_loader, clf1) # save results print( 'Epoch [%d/%d] Train Accuracy on the %s train data: Model %.4f %%' % (epoch + 1, args.n_epoch_1, len(train_dataset), train_acc)) print('Epoch [%d/%d] Val Accuracy on the %s val data: Model %.4f %% ' % (epoch + 1, args.n_epoch_1, len(val_dataset), val_acc)) print( 'Epoch [%d/%d] Test Accuracy on the %s test data: Model %.4f %% ' % (epoch + 1, args.n_epoch_1, len(test_dataset), test_acc)) with open(txtfile, "a") as myfile: myfile.write( str(int(epoch)) + ' ' + str(train_acc) + ' ' + str(val_acc) + ' ' + str(test_acc) + ' ' + "\n") if val_acc > best_acc: best_acc = val_acc torch.save(clf1.state_dict(), model_save_dir + '/' + 'model.pth') print('Matrix Factorization is doing...') clf1.load_state_dict(torch.load(model_save_dir + '/' + 'model.pth')) A = respresentations_extract(train_loader, clf1, len(train_dataset), args.dim, batch_size) A_val = respresentations_extract(val_loader, clf1, len(val_dataset), args.dim, batch_size) A_total = np.append(A, A_val, axis=0) W_total, H_total, error = train_m(A_total, args.basis, args.iteration_nmf, 1e-5) for i in range(W_total.shape[0]): for j in range(W_total.shape[1]): if W_total[i, j] < 1e-6: W_total[i, j] = 0. W = W_total[0:len(train_dataset), :] W_val = W_total[len(train_dataset):, :] print('Transition Matrix is estimating...Wating...') logits_matrix = probability_extract(train_loader, clf1, len(train_dataset), args.num_classes, batch_size) idx_matrix_group, transition_matrix_group = estimate_matrix( logits_matrix, model_save_dir) logits_matrix_val = probability_extract(val_loader, clf1, len(val_dataset), args.num_classes, batch_size) idx_matrix_group_val, transition_matrix_group_val = estimate_matrix( logits_matrix_val, model_save_dir) func = nn.MSELoss() model = Matrix_optimize(args.basis, args.num_classes) optimizer_1 = torch.optim.Adam(model.parameters(), lr=0.001) basis_matrix_group = basis_matrix_optimize(model, optimizer_1, args.basis, args.num_classes, W, transition_matrix_group, idx_matrix_group, func, model_save_dir, args.n_epoch_4) basis_matrix_group_val = basis_matrix_optimize( model, optimizer_1, args.basis, args.num_classes, W_val, transition_matrix_group_val, idx_matrix_group_val, func, model_save_dir, args.n_epoch_4) for i in range(basis_matrix_group.shape[0]): for j in range(basis_matrix_group.shape[1]): for k in range(basis_matrix_group.shape[2]): if basis_matrix_group[i, j, k] < 1e-6: basis_matrix_group[i, j, k] = 0. optimizer_ = torch.optim.SGD(clf1.parameters(), lr=args.lr, weight_decay=args.weight_decay, momentum=args.momentum) best_acc = 0.0 for epoch in range(1, args.n_epoch_2): # train model clf1.train() train_acc = train_correction(clf1, train_loader, epoch, optimizer_, W, basis_matrix_group, batch_size, args.num_classes, args.basis) # validation val_acc = val_correction(clf1, val_loader, epoch, W_val, basis_matrix_group_val, batch_size, args.num_classes, args.basis) # evaluate models test_acc = evaluate(test_loader, clf1) if val_acc > best_acc: best_acc = val_acc torch.save(clf1.state_dict(), model_save_dir + '/' + 'model.pth') with open(txtfile, "a") as myfile: myfile.write( str(int(epoch)) + ' ' + str(train_acc) + ' ' + str(val_acc) + ' ' + str(test_acc) + ' ' + "\n") # save results print( 'Epoch [%d/%d] Train Accuracy on the %s train data: Model %.4f %%' % (epoch + 1, args.n_epoch_2, len(train_dataset), train_acc)) print('Epoch [%d/%d] Val Accuracy on the %s val data: Model %.4f %% ' % (epoch + 1, args.n_epoch_2, len(val_dataset), val_acc)) print( 'Epoch [%d/%d] Test Accuracy on the %s test data: Model %.4f %% ' % (epoch + 1, args.n_epoch_2, len(test_dataset), test_acc)) clf1.load_state_dict(torch.load(model_save_dir + '/' + 'model.pth')) optimizer_r = torch.optim.Adam(clf1.parameters(), lr=args.lr_revision, weight_decay=args.weight_decay) nn.init.constant_(clf1.T_revision.weight, 0.0) for epoch in range(1, args.n_epoch_3): # train models clf1.train() train_acc = train_revision(clf1, train_loader, epoch, optimizer_r, W, basis_matrix_group, batch_size, args.num_classes, args.basis) # validation val_acc = val_revision(clf1, val_loader, epoch, W_val, basis_matrix_group, batch_size, args.num_classes, args.basis) # evaluate models test_acc = evaluate(test_loader, clf1) with open(txtfile, "a") as myfile: myfile.write( str(int(epoch)) + ' ' + str(train_acc) + ' ' + str(val_acc) + ' ' + str(test_acc) + ' ' + "\n") # save results print( 'Epoch [%d/%d] Train Accuracy on the %s train data: Model %.4f %%' % (epoch + 1, args.n_epoch_3, len(train_dataset), train_acc)) print('Epoch [%d/%d] Val Accuracy on the %s val data: Model %.4f %% ' % (epoch + 1, args.n_epoch_3, len(val_dataset), val_acc)) print( 'Epoch [%d/%d] Test Accuracy on the %s test data: Model %.4f %% ' % (epoch + 1, args.n_epoch_3, len(test_dataset), test_acc))
if not os.path.exists(directory): os.makedirs(directory) writer_loss = SummaryWriter(gen_path(loss_path)) writer_acc = SummaryWriter(gen_path(acc_path)) trans_mnist = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) dataset_train = MNIST('./data/mnist/', train=True, download=True, transform=trans_mnist) dataset_test = MNIST('./data/mnist/', train=False, download=True, transform=trans_mnist) # sample users dict_users = split_noniid_shuffle(dataset_train, args.num_nodes) img_size = dataset_train[0][0].shape print(img_size) net_glob = LeNet().to(args.device) print(net_glob.fc1.weight.type()) print(net_glob) net_glob.train() # copy weights w_glob = net_glob.state_dict() w_glob_grad = w_glob # training #loss_train = [] w_locals = [w_glob for i in range(args.num_nodes)] for iter in range(args.epochs): loss_locals = []
from torch.utils.data import DataLoader from torchvision.transforms import ToTensor from test import test_img from torch.utils.tensorboard import SummaryWriter if __name__ == '__main__': args = args_parser() args.device = torch.device('cuda:{}'.format(args.gpu)) batch_size = 256 trans_mnist = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) train_dataset = MNIST('./data/mnist/', train=True, download=True, transform=trans_mnist) test_dataset = MNIST('./data/mnist/', train=False, download=True, transform=trans_mnist) train_loader = DataLoader(train_dataset, batch_size=batch_size) test_loader = DataLoader(test_dataset, batch_size=batch_size) model = LeNet().to(args.device) sgd = SGD(model.parameters(), lr=1e-1) cross_error = CrossEntropyLoss() epoch = 100 writer = SummaryWriter('./runs/t_centerlize') for _epoch in range(epoch): epoch_loss = [] for idx, (train_x, train_label) in enumerate(train_loader): train_x, train_label = train_x.to(args.device), train_label.to(args.device) #label_np = np.zeros((train_label.shape[0], 10)) sgd.zero_grad() predict_y = model(train_x.float()) _error = cross_error(predict_y, train_label.long()) _error.backward() sgd.step()
args.dataset, batch_size=args.batch_size, conv_net=True, data_augmentation=args.data_augmentation, num_workers=num_workers) if args.data_augmentation: print(' data augmentation') window_size = train_loader.dataset.data[0].shape[0] if len(train_loader.dataset.data[0].shape) == 3: num_input_channels = train_loader.dataset.data[0].shape[2] else: num_input_channels = 1 model = LeNet(num_input_channels=num_input_channels, window_size=window_size, bias=True).to(device) # Multi-GPU if num_workers > 1: model = nn.DataParallel(model) criterion = nn.CrossEntropyLoss() if __name__ == "__main__": # Save everything in a `ddict` SAV = ddict(args=args.__dict__) # Store training and test performance after each training epoch SAV.perf = ddict(tr=[], te=[])
# test_type = 'single task classification' test_type = 'multi tasks classification' if test_type == 'single task classification': root = './data' download = True # download MNIST dataset or not trans = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))]) train_set = dset.MNIST(root=root, train=True, transform=trans, download=download) test_set = dset.MNIST(root=root, train=False, transform=trans) model = LeNet() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9) model.compile(optimizer, criterion, metrics=['top1', 'top2']) elif test_type == 'multi tasks classification': train_set = CategoricalDatasetMultiTasks(N, D_in, D_out1, D_out2) test_set = CategoricalDatasetMultiTasks( int(N * 0.25), D_in, D_out1, D_out2) model = MultiTasksClassification(D_in, H1, H2, D_out1, D_out2) optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
def main(args): num_classes = 10 size = [28, 28] # size of images ctype = ecvl.ColorType.GRAY in_ = eddl.Input([1, size[0], size[1]]) out = LeNet(in_, num_classes) net = eddl.Model([in_], [out]) eddl.build(net, eddl.sgd(0.001, 0.9), ["soft_cross_entropy"], ["categorical_accuracy"], eddl.CS_GPU([1]) if args.gpu else eddl.CS_CPU()) eddl.summary(net) eddl.setlogfile(net, "mnist") training_augs = ecvl.SequentialAugmentationContainer([ ecvl.AugRotate([-5, 5]), ecvl.AugAdditivePoissonNoise([0, 10]), ecvl.AugGaussianBlur([0, 0.8]), ecvl.AugCoarseDropout([0, 0.3], [0.02, 0.05], 0), ]) dataset_augs = ecvl.DatasetAugmentations([training_augs, None, None]) print("Reading dataset") d = ecvl.DLDataset(args.in_ds, args.batch_size, dataset_augs, ctype) x_train = Tensor([args.batch_size, d.n_channels_, size[0], size[1]]) y_train = Tensor([args.batch_size, len(d.classes_)]) num_samples = len(d.GetSplit()) num_batches = num_samples // args.batch_size indices = list(range(args.batch_size)) print("Training") for i in range(args.epochs): eddl.reset_loss(net) s = d.GetSplit() random.shuffle(s) d.split_.training_ = s d.ResetCurrentBatch() for j in range(num_batches): print("Epoch %d/%d (batch %d/%d) - " % (i + 1, args.epochs, j + 1, num_batches), end="", flush=True) d.LoadBatch(x_train, y_train) x_train.div_(255.0) tx, ty = [x_train], [y_train] eddl.train_batch(net, tx, ty, indices) eddl.print_loss(net, j) print() print("Saving weights") eddl.save(net, "mnist_checkpoint.bin", "bin") print("Evaluation") d.SetSplit(ecvl.SplitType.test) num_samples = len(d.GetSplit()) num_batches = num_samples // args.batch_size for i in range(num_batches): print("batch %d / %d - " % (i, num_batches), end="", flush=True) d.LoadBatch(x_train, y_train) x_train.div_(255.0) eddl.evaluate(net, [x_train], [y_train])
fmnist_train = datasets.FashionMNIST(FMNIST_DATA_HOME, train=True, transform=train_transform, download=True) fmnist_test = datasets.FashionMNIST(FMNIST_DATA_HOME, train=False, transform=test_transform, download=True) m = 5000 train_data = Subset(fmnist_train, m) val_data = Subset(fmnist_train, np.arange(m, m + 200)) val_data2 = Subset(fmnist_train, np.arange(m - 200, m)) test_data = fmnist_test net = LeNet(in_channels=1) optimizer = SGD(net.parameters(), lr=0.003, momentum=0.9, weight_decay=5e-4) batch_size = 64 data_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True) epochs = 100 for epoch in range(epochs): if epoch % 2 == 0: gc.collect() print("Epoch %d" % (epoch + 1)) for i, batch in enumerate(data_loader): inputs, labels = batch inputs = H.tensor(inputs.numpy()) labels = H.tensor(labels.numpy()) net.zero_grad()
def main(): opt = Parser(train=False).get() # dataset and data loader _, val_loader, adv_val_loader, _, num_classes = \ load_dataset(opt.dataset, opt.batch_size, opt.data_root, False, 0.0, opt.num_val_samples, workers=4) # model if opt.arch == 'lenet': model = LeNet(num_classes) elif opt.arch == 'resnet': model = ResNetv2_20(num_classes) else: raise NotImplementedError # move model to device model.to(opt.device) # load trained weight try: model.load_state_dict(torch.load(opt.weight_path)) except: model_weight = convert_model_from_parallel(opt.weight_path) model.load_state_dict(model_weight) # criterion criterion = nn.CrossEntropyLoss() # advertorch attacker if opt.attack == 'pgd': attacker = PGDAttack(model, loss_fn=criterion, eps=opt.eps / 255, nb_iter=opt.num_steps, eps_iter=opt.eps_iter / 255, rand_init=True, clip_min=opt.clip_min, clip_max=opt.clip_max, ord=np.inf, targeted=False) else: raise NotImplementedError # trainer trainer = Trainer(opt, model, criterion, attacker) trainer.print_freq = -1 # validation val_losses, val_acc1s, val_acc5s = \ trainer.validate(val_loader) aval_losses, aval_acc1s, aval_acc5s = \ trainer.adv_validate(adv_val_loader) print('[model] {}'.format(opt.weight_path)) print('[standard]\n' 'loss: {:.4f} | acc1: {:.2f}% | acc5: {:.2f}%' '\n[adversarial]\n' 'loss: {:.4f} | acc1: {:.2f}% | acc5: {:.2f}%'.format( val_losses['val'].avg, val_acc1s['val'].avg, val_acc5s['val'].avg, aval_losses['aval'].avg, aval_acc1s['aval'].avg, aval_acc5s['aval'].avg))
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test) testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2) classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') # Model print('==> Building model..') model = LeNet(3, len(classes)).to(device) if device.type == 'cuda': model = torch.nn.DataParallel(model) cudnn.benchmark = True if os.path.isfile(ckpt_file): checkpoint = torch.load(ckpt_file) model.load_state_dict(checkpoint['net']) best_acc = checkpoint['acc'] start_epoch = checkpoint['epoch'] criterion = nn.NLLLoss() optimizer = optim.SGD(model.parameters(), lr=1e-3, momentum=0.9,
returns={'processed_labels'}, resize=(img_height, img_width)) print("Number of images in the dataset:", train_dataset.get_n_samples()) print("Number of images in the dataset:", validation_dataset.get_n_samples()) # In[8]: steps_per_epoch = train_dataset.get_n_samples() / train_batch_size validation_steps = validation_dataset.get_n_samples() / validation_batch_size # In[9]: model = LeNet(n_classes=1, img_width=img_width, img_depth=img_depth, img_height=img_height, activation=activation) # In[10]: model.summary() # In[11]: optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.00001, amsgrad=True)
def main(): # Training settings parser = argparse.ArgumentParser(description='Adversarial MNIST Example') parser.add_argument('--use-pretrained', action='store_true', default=False, help='uses the pretrained model') parser.add_argument('--adversarial-training', action='store_true', default=False, help='takes the adversarial training process') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=512, metavar='N', help='input batch size for testing (default: 512)') args = parser.parse_args() # Define what device we are using use_cuda = torch.cuda.is_available() print("CUDA Available: ", use_cuda) device = torch.device("cuda" if use_cuda else "cpu") # MNIST Test dataset and dataloader declaration kwargs = {'num_workers': 1, '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()])), batch_size=args.batch_size, shuffle=True, **kwargs) test_loader = torch.utils.data.DataLoader(datasets.MNIST( '../data', train=False, transform=transforms.Compose([transforms.ToTensor()])), batch_size=args.test_batch_size, shuffle=True, **kwargs) # Initialize the network model = LeNet().to(device) optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5) if args.use_pretrained: print('Loading the pretrained model') model.load_state_dict( torch.load('resources/lenet_mnist_model.bin', map_location='cpu')) else: print('Training on the MNIST dataset') model_train(model, train_loader, F.nll_loss, optimizer, epochs=10) print('Evaluating the neural network') # Evaluate the accuracy of the MNIST model on clean examples accuracy, _ = model_eval(model, test_loader, F.nll_loss) print('Test accuracy on clean examples: ' + str(accuracy)) # Evaluate the accuracy of the MNIST model on adversarial examples accuracy, _ = model_eval(model, test_loader, F.nll_loss, attack_method=fgsm_attack) print('Test accuracy on adversarial examples: ' + str(accuracy)) if args.adversarial_training: print("Repeating the process, with adversarial training") # Perform adversarial training model_train(model, train_loader, F.nll_loss, optimizer, epochs=10, attack_method=fgsm_attack) # Evaluate the accuracy of the adversarially trained MNIST model on # clean examples accuracy, _ = model_eval(model, test_loader, F.nll_loss) print('Test accuracy on clean examples: ' + str(accuracy)) # Evaluate the accuracy of the adversarially trained MNIST model on # adversarial examples accuracy_adv, _ = model_eval(model, test_loader, F.nll_loss, attack_method=fgsm_attack) print('Test accuracy on adversarial examples: ' + str(accuracy_adv))
def main(): use_cuda = torch.cuda.is_available() and not args.no_cuda device = torch.device('cuda' if use_cuda else 'cpu') print(device) random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if use_cuda: torch.cuda.manual_seed(args.seed) torch.backends.cudnn.deterministic = True rgb = False if args.mode == 'rgb': rgb = True if args.gray_scale: rgb = False if args.tracking_data_mod is True: args.input_size = 192 # DATALOADER train_dataset = GesturesDataset(model=args.model, csv_path='csv_dataset', train=True, mode=args.mode, rgb=rgb, normalization_type=1, n_frames=args.n_frames, resize_dim=args.input_size, transform_train=args.train_transforms, tracking_data_mod=args.tracking_data_mod) train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.n_workers) validation_dataset = GesturesDataset(model=args.model, csv_path='csv_dataset', train=False, mode=args.mode, rgb=rgb, normalization_type=1, n_frames=args.n_frames, resize_dim=args.input_size, tracking_data_mod=args.tracking_data_mod) validation_loader = DataLoader(validation_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.n_workers) # paramteri per la rete in_channels = args.n_frames if not rgb else args.n_frames * 3 n_classes = args.n_classes if args.model == 'LeNet': model = LeNet(input_channels=in_channels, input_size=args.input_size, n_classes=n_classes).to(device) elif args.model == 'AlexNet': model = AlexNet(input_channels=in_channels, input_size=args.input_size, n_classes=n_classes).to(device) elif args.model == 'AlexNetBN': model = AlexNetBN(input_channels=in_channels, input_size=args.input_size, n_classes=n_classes).to(device) elif args.model == "Vgg16": model = Vgg16(input_channels=in_channels, input_size=args.input_size, n_classes=n_classes).to(device) elif args.model == "Vgg16P": model = models.vgg16(pretrained=args.pretrained) for params in model.parameters(): params.requires_grad = False model.features._modules['0'] = nn.Conv2d(in_channels=in_channels, out_channels=64, kernel_size=(3, 3), stride=1, padding=1) model.classifier._modules['6'] = nn.Linear(4096, n_classes) # model.fc = torch.nn.Linear(model.fc.in_features, n_classes) model = model.to(device) elif args.model == "ResNet18P": model = models.resnet18(pretrained=args.pretrained) for params in model.parameters(): params.requires_grad = False model._modules['conv1'] = nn.Conv2d(in_channels, 64, 7, stride=2, padding=3) model.fc = torch.nn.Linear(model.fc.in_features, n_classes) model = model.to(device) elif args.model == "ResNet34P": model = models.resnet34(pretrained=args.pretrained) for params in model.parameters(): params.requires_grad = False model._modules['conv1'] = nn.Conv2d(in_channels, 64, 7, stride=2, padding=3) model.fc = torch.nn.Linear(model.fc.in_features, n_classes) model = model.to(device) elif args.model == "DenseNet121P": model = models.densenet121(pretrained=args.pretrained) for params in model.parameters(): params.requires_grad = False model.features._modules['conv0'] = nn.Conv2d(in_channels=in_channels, out_channels=64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3)) model.classifier = nn.Linear(in_features=1024, out_features=n_classes, bias=True) model = model.to(device) elif args.model == "DenseNet161P": model = models.densenet161(pretrained=args.pretrained) # for params in model.parameters(): # params.requires_grad = False model.features._modules['conv0'] = nn.Conv2d(in_channels=in_channels, out_channels=96, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3)) model.classifier = nn.Linear(in_features=2208, out_features=n_classes, bias=True) model = model.to(device) elif args.model == "DenseNet169P": model = models.densenet169(pretrained=args.pretrained) for params in model.parameters(): params.requires_grad = False model.features._modules['conv0'] = nn.Conv2d(in_channels=in_channels, out_channels=64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3)) model.classifier = nn.Linear(in_features=1664, out_features=n_classes, bias=True) model = model.to(device) elif args.model == "DenseNet201P": model = models.densenet201(pretrained=args.pretrained) for params in model.parameters(): params.requires_grad = False model.features._modules['conv0'] = nn.Conv2d(in_channels=in_channels, out_channels=64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3)) model.classifier = nn.Linear(in_features=1920, out_features=n_classes, bias=True) model = model.to(device) # RNN elif args.model == 'LSTM' or args.model == 'GRU': model = Rnn(rnn_type=args.model, input_size=args.input_size, hidden_size=args.hidden_size, batch_size=args.batch_size, num_classes=args.n_classes, num_layers=args.n_layers, final_layer=args.final_layer).to(device) # C3D elif args.model == 'C3D': if args.pretrained: model = C3D(rgb=rgb, num_classes=args.n_classes) # modifico parametri print('ok') model.load_state_dict(torch.load('c3d_weights/c3d.pickle', map_location=device), strict=False) # # for params in model.parameters(): # # params.requires_grad = False model.conv1 = nn.Conv3d(1 if not rgb else 3, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1)) # tolgo fc6 perchè 30 frames model.fc6 = nn.Linear(16384, 4096) # num classes 28672 (112*200) model.fc7 = nn.Linear(4096, 4096) # num classes model.fc8 = nn.Linear(4096, n_classes) # num classes model = model.to(device) # Conv-lstm elif args.model == 'Conv-lstm': model = ConvLSTM(input_size=(args.input_size, args.input_size), input_dim=1 if not rgb else 3, hidden_dim=[64, 64, 128], kernel_size=(3, 3), num_layers=args.n_layers, batch_first=True, ).to(device) elif args.model == 'DeepConvLstm': model = DeepConvLstm(input_channels_conv=1 if not rgb else 3, input_size_conv=args.input_size, n_classes=12, n_frames=args.n_frames, batch_size=args.batch_size).to(device) elif args.model == 'ConvGRU': model = ConvGRU(input_size=40, hidden_sizes=[64, 128], kernel_sizes=[3, 3], n_layers=2).to(device) else: raise NotImplementedError if args.opt == 'SGD': optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum) # optimizer = optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr, momentum=args.momentum) elif args.opt == 'Adam': optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) loss_function = nn.CrossEntropyLoss().to(device) start_epoch = 0 if args.resume: checkpoint = torch.load("/projects/fabio/weights/gesture_recog_weights/checkpoint{}.pth.tar".format(args.model)) model.load_state_dict(checkpoint['state_dict']) optimizer.load_state_dict(checkpoint['optimizer']) start_epoch = checkpoint['epoch'] print("Resuming state:\n-epoch: {}\n{}".format(start_epoch, model)) #name experiment personal_name = "{}_{}_{}".format(args.model, args.mode, args.exp_name) info_experiment = "{}".format(personal_name) log_dir = "/projects/fabio/logs/gesture_recog_logs/exps" weight_dir = personal_name log_file = open("{}/{}.txt".format("/projects/fabio/logs/gesture_recog_logs/txt_logs", personal_name), 'w') log_file.write(personal_name + "\n\n") if personal_name: exp_name = (("exp_{}_{}".format(time.strftime("%c"), personal_name)).replace(" ", "_")).replace(":", "-") else: exp_name = (("exp_{}".format(time.strftime("%c"), personal_name)).replace(" ", "_")).replace(":", "-") writer = SummaryWriter("{}".format(os.path.join(log_dir, exp_name))) # add info experiment writer.add_text('Info experiment', "model:{}" "\n\npretrained:{}" "\n\nbatch_size:{}" "\n\nepochs:{}" "\n\noptimizer:{}" "\n\nlr:{}" "\n\ndn_lr:{}" "\n\nmomentum:{}" "\n\nweight_decay:{}" "\n\nn_frames:{}" "\n\ninput_size:{}" "\n\nhidden_size:{}" "\n\ntracking_data_mode:{}" "\n\nn_classes:{}" "\n\nmode:{}" "\n\nn_workers:{}" "\n\nseed:{}" "\n\ninfo:{}" "".format(args.model, args.pretrained, args.batch_size, args.epochs, args.opt, args.lr, args.dn_lr, args.momentum, args.weight_decay, args.n_frames, args.input_size, args.hidden_size, args.tracking_data_mod, args.n_classes, args.mode, args.n_workers, args.seed, info_experiment)) trainer = Trainer(model=model, loss_function=loss_function, optimizer=optimizer, train_loader=train_loader, validation_loader=validation_loader, batch_size=args.batch_size, initial_lr=args.lr, device=device, writer=writer, personal_name=personal_name, log_file=log_file, weight_dir=weight_dir, dynamic_lr=args.dn_lr) print("experiment: {}".format(personal_name)) start = time.time() for ep in range(start_epoch, args.epochs): trainer.train(ep) trainer.val(ep) # display classes results classes = ['g0', 'g1', 'g2', 'g3', 'g4', 'g5', 'g6', 'g7', 'g8', 'g9', 'g10', 'g11'] for i in range(args.n_classes): print('Accuracy of {} : {:.3f}%%'.format( classes[i], 100 * trainer.class_correct[i] / trainer.class_total[i])) end = time.time() h, rem = divmod(end - start, 3600) m, s, = divmod(rem, 60) print("\nelapsed time (ep.{}):{:0>2}:{:0>2}:{:05.2f}".format(args.epochs, int(h), int(m), s)) # writing accuracy on file log_file.write("\n\n") for i in range(args.n_classes): log_file.write('Accuracy of {} : {:.3f}%\n'.format( classes[i], 100 * trainer.class_correct[i] / trainer.class_total[i])) log_file.close()
def main(): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, 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=0.01, metavar='LR', help='learning rate (default: 0.01)') 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') args = parser.parse_args() use_cuda = not args.no_cuda and torch.cuda.is_available() torch.manual_seed(args.seed) kwargs = {'num_workers': 1, '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((0.1307, ), (0.3081, )) ])), 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((0.1307, ), (0.3081, )) ])), batch_size=args.test_batch_size, shuffle=True, **kwargs) feature_blob = np.zeros([1, 16, 8, 8]) model = LeNet() def hook(module, input, output): global feature_blob feature_blob = output model._modules.get('conv2').register_forward_hook(hook) model = torch.nn.DataParallel(model).cuda() optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum) for epoch in range(1, args.epochs + 1): train(args, model, train_loader, optimizer, epoch) cam(model, epoch) test(args, model, test_loader) generate_gif()
def main(): opt = Parser().get() experiment = None if opt.comet: experiment = Experiment() experiment.set_name(opt.exp_name) experiment.log_parameters(opt.__dict__) experiment.add_tags(opt.add_tags) # dataset and data loader train_loader, val_loader, adv_val_loader, _, num_classes = \ load_dataset(opt.dataset, opt.batch_size, opt.data_root, opt.noise, opt.noise_std, opt.num_val_samples, workers=4) # model if opt.arch == 'lenet': model = LeNet(num_classes) elif opt.arch == 'resnet': model = ResNetv2_20(num_classes) else: raise NotImplementedError # weight init if opt.weight_init == 'he': model.apply(init_he) # move model to device model.to(opt.device) if opt.gpu_ids: model = nn.DataParallel(model, device_ids=opt.gpu_ids) # criterion criterion = nn.CrossEntropyLoss() # advertorch attacker if opt.attack == 'pgd': attacker = LinfPGDAttack(model, loss_fn=criterion, eps=opt.eps / 255, nb_iter=opt.num_steps, eps_iter=opt.eps_iter / 255, rand_init=True, clip_min=opt.clip_min, clip_max=opt.clip_max, targeted=False) else: raise NotImplementedError # optimizer if opt.optim == 'Adam': optimizer = optim.Adam(model.parameters(), opt.lr, eps=1e-6, weight_decay=opt.wd) elif opt.optim == 'SGD': optimizer = optim.SGD(model.parameters(), opt.lr, weight_decay=opt.wd) else: raise NotImplementedError # scheduler if opt.scheduler_step: scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=opt.scheduler_step, gamma=opt.scheduler_gamma) else: scheduler = None # timer timer = Timer(opt.num_epochs, 0) # trainer trainer = Trainer(opt, model, criterion, attacker, optimizer) # epoch iteration for epoch in range(1, opt.num_epochs + 1): trainer.epoch = epoch if scheduler: scheduler.step(epoch - 1) # scheduler's epoch is 0-indexed. # training train_losses, train_acc1s, train_acc5s = \ trainer.train(train_loader) # validation val_losses, val_acc1s, val_acc5s = \ trainer.validate(val_loader) if opt.adv_val_freq != -1 and epoch % opt.adv_val_freq == 0: aval_losses, aval_acc1s, aval_acc5s = \ trainer.adv_validate(adv_val_loader) else: aval_losses, aval_acc1s, aval_acc5s = \ dict(), dict(), dict() losses = dict(**train_losses, **val_losses, **aval_losses) acc1s = dict(**train_acc1s, **val_acc1s, **aval_acc1s) acc5s = dict(**train_acc5s, **val_acc5s, **aval_acc5s) report_epoch_status(losses, acc1s, acc5s, trainer.num_loss, epoch, opt, timer, experiment) save_path = os.path.join('ckpt', opt.dataset, 'models', opt.exp_name + '.pth') trainer.save_model(save_path)