def get_model(train_model): if train_model == 'resnet18': return resnet.resnet18() elif train_model == 'resnet34': return resnet.resnet34() elif train_model == 'resnet50': return resnet.resnet50() elif train_model == 'resnet101': return resnet.resnet101() elif train_model == 'resnet152': return resnet.resnet152() elif train_model == 'resnet18_copy': return resnet_copy.resnet18() elif train_model == 'resnet34_copy': return resnet_copy.resnet34() elif train_model == 'resnet50_copy': return resnet_copy.resnet50() elif train_model == 'resnet101_copy': return resnet_copy.resnet101() elif train_model == 'resnet152': return resnet_copy.resnet152() elif train_model == 'vgg11': return vgg11() elif train_model == 'vgg13': return vgg13() elif train_model == 'vgg16': return vgg16() elif train_model == 'vgg19': return vgg19() elif train_model == 'nin': return nin() elif train_model == 'googlenet': return googlenet()
def train_with_augmentation(): datagen = ImageDataGenerator(rescale=1. / 255, rotation_range=30., horizontal_flip=True) model = vgg13() (X_train, Y_train), (X_test, Y_test), (X_validation, Y_validation) = fer2013() if load_weights: model.load_weights('model_vgg_13_aug.h5') history = model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size), samples_per_epoch=50000, nb_epoch=nb_epoch, validation_data=(X_validation, Y_validation)) predictions = model.predict(X_test, batch_size=batch_size, verbose=1) historic(history) confusion_matrix(predictions, Y_test) if save_weights: model.save_weights('model_vgg_13_aug.h5')
def get_network(args,cfg): """ return given network """ # pdb.set_trace() if args.net == 'lenet5': net = LeNet5().cuda() elif args.net == 'alexnet': net = alexnet(pretrained=args.pretrain, num_classes=cfg.PARA.train.num_classes).cuda() elif args.net == 'vgg16': net = vgg16(pretrained=args.pretrain, num_classes=cfg.PARA.train.num_classes).cuda() elif args.net == 'vgg13': net = vgg13(pretrained=args.pretrain,num_classes=cfg.PARA.train.num_classes).cuda() elif args.net == 'vgg11': net = vgg11(pretrained=args.pretrain,num_classes=cfg.PARA.train.num_classes).cuda() elif args.net == 'vgg19': net = vgg19(pretrained=args.pretrain,num_classes=cfg.PARA.train.num_classes).cuda() elif args.net == 'vgg16_bn': net = vgg16_bn(pretrained=args.pretrain,num_classes=cfg.PARA.train.num_classes).cuda() elif args.net == 'vgg13_bn': net = vgg13_bn(pretrained=args.pretrain,num_classes=cfg.PARA.train.num_classes).cuda() elif args.net == 'vgg11_bn': net = vgg11_bn(pretrained=args.pretrain,num_classes=cfg.PARA.train.num_classes).cuda() elif args.net == 'vgg19_bn': net = vgg19_bn(pretrained=args.pretrain,num_classes=cfg.PARA.train.num_classes).cuda() elif args.net =='inceptionv3': net = inception_v3().cuda() # elif args.net == 'inceptionv4': # net = inceptionv4().cuda() # elif args.net == 'inceptionresnetv2': # net = inception_resnet_v2().cuda() elif args.net == 'resnet18': net = resnet18(pretrained=args.pretrain,num_classes=cfg.PARA.train.num_classes).cuda(args.gpuid) elif args.net == 'resnet34': net = resnet34(pretrained=args.pretrain,num_classes=cfg.PARA.train.num_classes).cuda() elif args.net == 'resnet50': net = resnet50(pretrained=args.pretrain,num_classes=cfg.PARA.train.num_classes).cuda(args.gpuid) elif args.net == 'resnet101': net = resnet101(pretrained=args.pretrain,num_classes=cfg.PARA.train.num_classes).cuda() elif args.net == 'resnet152': net = resnet152(pretrained=args.pretrain,num_classes=cfg.PARA.train.num_classes).cuda() elif args.net == 'squeezenet': net = squeezenet1_0().cuda() else: print('the network name you have entered is not supported yet') sys.exit() return net
def get_network(args): """ return given network """ if args.task == 'cifar10': nclass = 10 elif args.task == 'cifar100': nclass = 100 #Yang added none bn vggs if args.net == 'vgg11': from models.vgg import vgg11 net = vgg11(num_classes=nclass) elif args.net == 'vgg13': from models.vgg import vgg13 net = vgg13(num_classes=nclass) elif args.net == 'vgg16': from models.vgg import vgg16 net = vgg16(num_classes=nclass) elif args.net == 'vgg19': from models.vgg import vgg19 net = vgg19(num_classes=nclass) elif args.net == 'resnet18': from models.resnet import resnet18 net = resnet18(num_classes=nclass) elif args.net == 'resnet34': from models.resnet import resnet34 net = resnet34(num_classes=nclass) elif args.net == 'resnet50': from models.resnet import resnet50 net = resnet50(num_classes=nclass) elif args.net == 'resnet101': from models.resnet import resnet101 net = resnet101(num_classes=nclass) elif args.net == 'resnet152': from models.resnet import resnet152 net = resnet152(num_classes=nclass) else: print('the network name you have entered is not supported yet') sys.exit() if args.gpu: #use_gpu net = net.cuda() return net
def get_network(args): if args.net == 'vgg16': from models.vgg import vgg16 net = vgg16() elif args.net == 'vgg11': from models.vgg import vgg11 net = vgg11() elif args.net == 'vgg13': from models.vgg import vgg13 net = vgg13() elif args.net == 'vgg19': from models.vgg import vgg19 net = vgg19() return net
def get_model(class_num): if (MODEL_TYPE == 'alexnet'): model = alexnet.alexnet(pretrained=FINETUNE) elif (MODEL_TYPE == 'vgg'): if (MODEL_DEPTH_OR_VERSION == 11): model = vgg.vgg11(pretrained=FINETUNE) elif (MODEL_DEPTH_OR_VERSION == 13): model = vgg.vgg13(pretrained=FINETUNE) elif (MODEL_DEPTH_OR_VERSION == 16): model = vgg.vgg16(pretrained=FINETUNE) elif (MODEL_DEPTH_OR_VERSION == 19): model = vgg.vgg19(pretrained=FINETUNE) else: print('Error : VGG should have depth of either [11, 13, 16, 19]') sys.exit(1) elif (MODEL_TYPE == 'squeezenet'): if (MODEL_DEPTH_OR_VERSION == 0 or MODEL_DEPTH_OR_VERSION == 'v0'): model = squeezenet.squeezenet1_0(pretrained=FINETUNE) elif (MODEL_DEPTH_OR_VERSION == 1 or MODEL_DEPTH_OR_VERSION == 'v1'): model = squeezenet.squeezenet1_1(pretrained=FINETUNE) else: print('Error : Squeezenet should have version of either [0, 1]') sys.exit(1) elif (MODEL_TYPE == 'resnet'): if (MODEL_DEPTH_OR_VERSION == 18): model = resnet.resnet18(pretrained=FINETUNE) elif (MODEL_DEPTH_OR_VERSION == 34): model = resnet.resnet34(pretrained=FINETUNE) elif (MODEL_DEPTH_OR_VERSION == 50): model = resnet.resnet50(pretrained=FINETUNE) elif (MODEL_DEPTH_OR_VERSION == 101): model = resnet.resnet101(pretrained=FINETUNE) elif (MODEL_DEPTH_OR_VERSION == 152): model = resnet.resnet152(pretrained=FINETUNE) else: print( 'Error : Resnet should have depth of either [18, 34, 50, 101, 152]' ) sys.exit(1) elif (MODEL_TYPE == 'densenet'): if (MODEL_DEPTH_OR_VERSION == 121): model = densenet.densenet121(pretrained=FINETUNE) elif (MODEL_DEPTH_OR_VERSION == 169): model = densenet.densenet169(pretrained=FINETUNE) elif (MODEL_DEPTH_OR_VERSION == 161): model = densenet.densenet161(pretrained=FINETUNE) elif (MODEL_DEPTH_OR_VERSION == 201): model = densenet.densenet201(pretrained=FINETUNE) else: print( 'Error : Densenet should have depth of either [121, 169, 161, 201]' ) sys.exit(1) elif (MODEL_TYPE == 'inception'): if (MODEL_DEPTH_OR_VERSION == 3 or MODEL_DEPTH_OR_VERSION == 'v3'): model = inception.inception_v3(pretrained=FINETUNE) else: print('Error : Inception should have version of either [3, ]') sys.exit(1) else: print( 'Error : Network should be either [alexnet / squeezenet / vgg / resnet / densenet / inception]' ) sys.exit(1) if (MODEL_TYPE == 'alexnet' or MODEL_TYPE == 'vgg'): num_ftrs = model.classifier[6].in_features feature_model = list(model.classifier.children()) feature_model.pop() feature_model.append(nn.Linear(num_ftrs, class_num)) model.classifier = nn.Sequential(*feature_model) elif (MODEL_TYPE == 'resnet' or MODEL_TYPE == 'inception'): num_ftrs = model.fc.in_features model.fc = nn.Linear(num_ftrs, class_num) elif (MODEL_TYPE == 'densenet'): num_ftrs = model.classifier.in_features model.classifier = nn.Linear(num_ftrs, class_num) return model
def get_model(args, model_path=None): """ :param args: super arguments :param model_path: if not None, load already trained model parameters. :return: model """ if args.scratch: # train model from scratch pretrained = False model_dir = None print("=> Loading model '{}' from scratch...".format(args.model)) else: # train model with pretrained model pretrained = True model_dir = os.path.join(args.root_path, args.pretrained_models_path) print("=> Loading pretrained model '{}'...".format(args.model)) if args.model.startswith('resnet'): if args.model == 'resnet18': model = resnet18(pretrained=pretrained, model_dir=model_dir) elif args.model == 'resnet34': model = resnet34(pretrained=pretrained, model_dir=model_dir) elif args.model == 'resnet50': model = resnet50(pretrained=pretrained, model_dir=model_dir) elif args.model == 'resnet101': model = resnet101(pretrained=pretrained, model_dir=model_dir) elif args.model == 'resnet152': model = resnet152(pretrained=pretrained, model_dir=model_dir) model.fc = nn.Linear(model.fc.in_features, args.num_classes) elif args.model.startswith('vgg'): if args.model == 'vgg11': model = vgg11(pretrained=pretrained, model_dir=model_dir) elif args.model == 'vgg11_bn': model = vgg11_bn(pretrained=pretrained, model_dir=model_dir) elif args.model == 'vgg13': model = vgg13(pretrained=pretrained, model_dir=model_dir) elif args.model == 'vgg13_bn': model = vgg13_bn(pretrained=pretrained, model_dir=model_dir) elif args.model == 'vgg16': model = vgg16(pretrained=pretrained, model_dir=model_dir) elif args.model == 'vgg16_bn': model = vgg16_bn(pretrained=pretrained, model_dir=model_dir) elif args.model == 'vgg19': model = vgg19(pretrained=pretrained, model_dir=model_dir) elif args.model == 'vgg19_bn': model = vgg19_bn(pretrained=pretrained, model_dir=model_dir) model.classifier[6] = nn.Linear(model.classifier[6].in_features, args.num_classes) elif args.model == 'alexnet': model = alexnet(pretrained=pretrained, model_dir=model_dir) model.classifier[6] = nn.Linear(model.classifier[6].in_features, args.num_classes) # Load already trained model parameters and go on training if model_path is not None: checkpoint = torch.load(model_path) model.load_state_dict(checkpoint['model']) return model
def main(): global args, best_err1 args = parser.parse_args() # TensorBoard configure if args.tensorboard: configure('%s_checkpoints/%s'%(args.dataset, args.expname)) # CUDA os.environ['CUDA_DEVICE_ORDER'] = "PCI_BUS_ID" os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_ids) if torch.cuda.is_available(): cudnn.benchmark = True # https://discuss.pytorch.org/t/what-does-torch-backends-cudnn-benchmark-do/5936 kwargs = {'num_workers': 2, 'pin_memory': True} else: kwargs = {'num_workers': 2} # Data loading code if args.dataset == 'cifar10': normalize = transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010]) elif args.dataset == 'cifar100': normalize = transforms.Normalize(mean=[0.5071, 0.4865, 0.4409], std=[0.2634, 0.2528, 0.2719]) elif args.dataset == 'cub': normalize = transforms.Normalize(mean=[0.4862, 0.4973, 0.4293], std=[0.2230, 0.2185, 0.2472]) elif args.dataset == 'webvision': normalize = transforms.Normalize(mean=[0.49274242, 0.46481857, 0.41779366], std=[0.26831809, 0.26145372, 0.27042758]) else: raise Exception('Unknown dataset: {}'.format(args.dataset)) # Transforms if args.augment: train_transform = transforms.Compose([ transforms.RandomResizedCrop(args.train_image_size), transforms.RandomHorizontalFlip(), transforms.ToTensor(), normalize, ]) else: train_transform = transforms.Compose([ transforms.RandomResizedCrop(args.train_image_size), transforms.ToTensor(), normalize, ]) val_transform = transforms.Compose([ transforms.Resize(args.test_image_size), transforms.CenterCrop(args.test_crop_image_size), transforms.ToTensor(), normalize ]) # Datasets num_classes = 10 # default 10 classes if args.dataset == 'cifar10': train_dataset = datasets.CIFAR10('./data/', train=True, download=True, transform=train_transform) val_dataset = datasets.CIFAR10('./data/', train=False, download=True, transform=val_transform) num_classes = 10 elif args.dataset == 'cifar100': train_dataset = datasets.CIFAR100('./data/', train=True, download=True, transform=train_transform) val_dataset = datasets.CIFAR100('./data/', train=False, download=True, transform=val_transform) num_classes = 100 elif args.dataset == 'cub': train_dataset = datasets.ImageFolder('/media/ouc/30bd7817-d3a1-4e83-b7d9-5c0e373ae434/DuAngAng/datasets/CUB-200-2011/train/', transform=train_transform) val_dataset = datasets.ImageFolder('/media/ouc/30bd7817-d3a1-4e83-b7d9-5c0e373ae434/DuAngAng/datasets/CUB-200-2011/test/', transform=val_transform) num_classes = 200 elif args.dataset == 'webvision': train_dataset = datasets.ImageFolder('/media/ouc/30bd7817-d3a1-4e83-b7d9-5c0e373ae434/LiuJing/WebVision/info/train', transform=train_transform) val_dataset = datasets.ImageFolder('/media/ouc/30bd7817-d3a1-4e83-b7d9-5c0e373ae434/LiuJing/WebVision/info/val', transform=val_transform) num_classes = 1000 else: raise Exception('Unknown dataset: {}'.format(args.dataset)) # Data Loader train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.batchsize, shuffle=True, **kwargs) val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=False, **kwargs) # Create model if args.model == 'AlexNet': model = alexnet(pretrained=False, num_classes=num_classes) elif args.model == 'VGG': use_batch_normalization = True # default use Batch Normalization if use_batch_normalization: if args.depth == 11: model = vgg11_bn(pretrained=False, num_classes=num_classes) elif args.depth == 13: model = vgg13_bn(pretrained=False, num_classes=num_classes) elif args.depth == 16: model = vgg16_bn(pretrained=False, num_classes=num_classes) elif args.depth == 19: model = vgg19_bn(pretrained=False, num_classes=num_classes) else: raise Exception('Unsupport VGG detph: {}, optional depths: 11, 13, 16 or 19'.format(args.depth)) else: if args.depth == 11: model = vgg11(pretrained=False, num_classes=num_classes) elif args.depth == 13: model = vgg13(pretrained=False, num_classes=num_classes) elif args.depth == 16: model = vgg16(pretrained=False, num_classes=num_classes) elif args.depth == 19: model = vgg19(pretrained=False, num_classes=num_classes) else: raise Exception('Unsupport VGG detph: {}, optional depths: 11, 13, 16 or 19'.format(args.depth)) elif args.model == 'Inception': model = inception_v3(pretrained=False, num_classes=num_classes) elif args.model == 'ResNet': if args.depth == 18: model = resnet18(pretrained=False, num_classes=num_classes) elif args.depth == 34: model = resnet34(pretrained=False, num_classes=num_classes) elif args.depth == 50: model = resnet50(pretrained=False, num_classes=num_classes) elif args.depth == 101: model = resnet101(pretrained=False, num_classes=num_classes) elif args.depth == 152: model = resnet152(pretrained=False, num_classes=num_classes) else: raise Exception('Unsupport ResNet detph: {}, optional depths: 18, 34, 50, 101 or 152'.format(args.depth)) elif args.model == 'MPN-COV-ResNet': if args.depth == 18: model = mpn_cov_resnet18(pretrained=False, num_classes=num_classes) elif args.depth == 34: model = mpn_cov_resnet34(pretrained=False, num_classes=num_classes) elif args.depth == 50: model = mpn_cov_resnet50(pretrained=False, num_classes=num_classes) elif args.depth == 101: model = mpn_cov_resnet101(pretrained=False, num_classes=num_classes) elif args.depth == 152: model = mpn_cov_resnet152(pretrained=False, num_classes=num_classes) else: raise Exception('Unsupport MPN-COV-ResNet detph: {}, optional depths: 18, 34, 50, 101 or 152'.format(args.depth)) else: raise Exception('Unsupport model'.format(args.model)) # Get the number of model parameters print('Number of model parameters: {}'.format(sum([p.data.nelement() for p in model.parameters()]))) if torch.cuda.is_available(): model = model.cuda() # Optionally resume from a checkpoint if args.resume: if os.path.isfile(args.resume): print("==> loading checkpoint '{}'".format(args.resume)) checkpoint = torch.load(args.resume) args.start_epoch = checkpoint['epoch'] best_err1 = checkpoint['best_err1'] model.load_state_dict(checkpoint['state_dict']) print("==> loaded checkpoint '{}' (epoch {})" .format(args.resume, checkpoint['epoch'])) else: print("==> no checkpoint found at '{}'".format(args.resume)) print(model) # Define loss function (criterion) and optimizer criterion = nn.CrossEntropyLoss() if torch.cuda.is_available(): criterion = criterion.cuda() optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=args.momentum, weight_decay=args.weight_decay, nesterov=True) for epoch in range(args.start_epoch, args.epochs): adjust_learning_rate(optimizer, epoch) # Train for one epoch train(train_loader, model, criterion, optimizer, epoch) # Evaluate on validation set err1 = validate(val_loader, model, criterion, epoch) # Remember best err1 and save checkpoint is_best = (err1 <= best_err1) best_err1 = min(err1, best_err1) print("Current best accuracy (error):", best_err1) save_checkpoint({ 'epoch': epoch+1, 'state_dict': model.state_dict(), 'best_err1': best_err1, }, is_best) print("Best accuracy (error):", best_err1)
def get_network(args): """ return given network """ if args.task == 'cifar10': nclass = 10 elif args.task == 'cifar100': nclass = 100 #Yang added none bn vggs if args.net == 'vgg16': from models.vgg import vgg16 net = vgg16(num_classes=nclass) elif args.net == 'vgg13': from models.vgg import vgg13 net = vgg13(num_classes=nclass) elif args.net == 'vgg11': from models.vgg import vgg11 net = vgg11(num_classes=nclass) elif args.net == 'vgg19': from models.vgg import vgg19 net = vgg19(num_classes=nclass) elif args.net == 'vgg16bn': from models.vgg import vgg16_bn net = vgg16_bn(num_classes=nclass) elif args.net == 'vgg13bn': from models.vgg import vgg13_bn net = vgg13_bn(num_classes=nclass) elif args.net == 'vgg11bn': from models.vgg import vgg11_bn net = vgg11_bn(num_classes=nclass) elif args.net == 'vgg19bn': from models.vgg import vgg19_bn net = vgg19_bn(num_classes=nclass) elif args.net == 'densenet121': from models.densenet import densenet121 net = densenet121() elif args.net == 'densenet161': from models.densenet import densenet161 net = densenet161() elif args.net == 'densenet169': from models.densenet import densenet169 net = densenet169() elif args.net == 'densenet201': from models.densenet import densenet201 net = densenet201() elif args.net == 'googlenet': from models.googlenet import googlenet net = googlenet(num_classes=nclass) elif args.net == 'inceptionv3': from models.inceptionv3 import inceptionv3 net = inceptionv3() elif args.net == 'inceptionv4': from models.inceptionv4 import inceptionv4 net = inceptionv4() elif args.net == 'inceptionresnetv2': from models.inceptionv4 import inception_resnet_v2 net = inception_resnet_v2() elif args.net == 'xception': from models.xception import xception net = xception(num_classes=nclass) elif args.net == 'scnet': from models.sphereconvnet import sphereconvnet net = sphereconvnet(num_classes=nclass) elif args.net == 'sphereresnet18': from models.sphereconvnet import resnet18 net = resnet18(num_classes=nclass) elif args.net == 'sphereresnet32': from models.sphereconvnet import sphereresnet32 net = sphereresnet32(num_classes=nclass) elif args.net == 'plainresnet32': from models.sphereconvnet import plainresnet32 net = plainresnet32(num_classes=nclass) elif args.net == 'ynet18': from models.ynet import resnet18 net = resnet18(num_classes=nclass) elif args.net == 'ynet34': from models.ynet import resnet34 net = resnet34(num_classes=nclass) elif args.net == 'ynet50': from models.ynet import resnet50 net = resnet50(num_classes=nclass) elif args.net == 'ynet101': from models.ynet import resnet101 net = resnet101(num_classes=nclass) elif args.net == 'ynet152': from models.ynet import resnet152 net = resnet152(num_classes=nclass) elif args.net == 'resnet18': from models.resnet import resnet18 net = resnet18(num_classes=nclass) elif args.net == 'resnet34': from models.resnet import resnet34 net = resnet34(num_classes=nclass) elif args.net == 'resnet50': from models.resnet import resnet50 net = resnet50(num_classes=nclass) elif args.net == 'resnet101': from models.resnet import resnet101 net = resnet101(num_classes=nclass) elif args.net == 'resnet152': from models.resnet import resnet152 net = resnet152(num_classes=nclass) elif args.net == 'preactresnet18': from models.preactresnet import preactresnet18 net = preactresnet18(num_classes=nclass) elif args.net == 'preactresnet34': from models.preactresnet import preactresnet34 net = preactresnet34(num_classes=nclass) elif args.net == 'preactresnet50': from models.preactresnet import preactresnet50 net = preactresnet50(num_classes=nclass) elif args.net == 'preactresnet101': from models.preactresnet import preactresnet101 net = preactresnet101(num_classes=nclass) elif args.net == 'preactresnet152': from models.preactresnet import preactresnet152 net = preactresnet152(num_classes=nclass) elif args.net == 'resnext50': from models.resnext import resnext50 net = resnext50(num_classes=nclass) elif args.net == 'resnext101': from models.resnext import resnext101 net = resnext101(num_classes=nclass) elif args.net == 'resnext152': from models.resnext import resnext152 net = resnext152(num_classes=nclass) elif args.net == 'shufflenet': from models.shufflenet import shufflenet net = shufflenet() elif args.net == 'shufflenetv2': from models.shufflenetv2 import shufflenetv2 net = shufflenetv2() elif args.net == 'squeezenet': from models.squeezenet import squeezenet net = squeezenet() elif args.net == 'mobilenet': from models.mobilenet import mobilenet net = mobilenet(num_classes=nclass) elif args.net == 'mobilenetv2': from models.mobilenetv2 import mobilenetv2 net = mobilenetv2(num_classes=nclass) elif args.net == 'nasnet': from models.nasnet import nasnet net = nasnet(num_classes=nclass) elif args.net == 'attention56': from models.attention import attention56 net = attention56() elif args.net == 'attention92': from models.attention import attention92 net = attention92() elif args.net == 'seresnet18': from models.senet import seresnet18 net = seresnet18(num_classes=nclass) elif args.net == 'seresnet34': from models.senet import seresnet34 net = seresnet34(num_classes=nclass) elif args.net == 'seresnet50': from models.senet import seresnet50 net = seresnet50(num_classes=nclass) elif args.net == 'seresnet101': from models.senet import seresnet101 net = seresnet101(num_classes=nclass) elif args.net == 'seresnet152': from models.senet import seresnet152 net = seresnet152(num_classes=nclass) else: print('the network name you have entered is not supported yet') sys.exit() if args.gpu: #use_gpu net = net.cuda() return net