def infer(): model_path = './resnext50_NWPU-RESISC45.pth' batch_size = 256 net = resnext50(num_classes=45).to(device) pretrain = torch.load(model_path) net.load_state_dict(pretrain) test_data = ClassifyData(root='./test_data') test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=16) tbar = tqdm(test_loader) test_accu = 0 net.eval() with torch.no_grad(): for i, (img, lab) in enumerate(tbar): tbar.set_description("Testing->>") img = img.to(device) b = img.size(0) prob = net.forward(img) pred = prob.data.max(1)[1].cpu() accu = float(pred.eq(lab.data).sum()) / b test_accu = ((test_accu * i) + accu) / (i + 1) print("test accuracy=%.6f" % test_accu)
def get_network(args): """ return given network """ if args.net == 'vgg16': from models.vgg import vgg16_bn net = vgg16_bn() elif args.net == 'vgg13': from models.vgg import vgg13_bn net = vgg13_bn() elif args.net == 'vgg11': from models.vgg import vgg11_bn net = vgg11_bn() elif args.net == 'vgg19': from models.vgg import vgg19_bn net = vgg19_bn() 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() 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() elif args.net == 'resnet18': from models.resnet import resnet18 net = resnet18() elif args.net == 'resnet34': from models.resnet import resnet34 net = resnet34() elif args.net == 'resnet50': from models.resnet import resnet50 net = resnet50() elif args.net == 'resnet101': from models.resnet import resnet101 net = resnet101() elif args.net == 'resnet152': from models.resnet import resnet152 net = resnet152() elif args.net == 'preactresnet18': from models.preactresnet import preactresnet18 net = preactresnet18() elif args.net == 'preactresnet34': from models.preactresnet import preactresnet34 net = preactresnet34() elif args.net == 'preactresnet50': from models.preactresnet import preactresnet50 net = preactresnet50() elif args.net == 'preactresnet101': from models.preactresnet import preactresnet101 net = preactresnet101() elif args.net == 'preactresnet152': from models.preactresnet import preactresnet152 net = preactresnet152() elif args.net == 'resnext50': from models.resnext import resnext50 net = resnext50() elif args.net == 'resnext101': from models.resnext import resnext101 net = resnext101() elif args.net == 'resnext152': from models.resnext import resnext152 net = resnext152() 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() elif args.net == 'mobilenetv2': from models.mobilenetv2 import mobilenetv2 net = mobilenetv2() elif args.net == 'nasnet': from models.nasnet import nasnet net = nasnet() 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() elif args.net == 'seresnet34': from models.senet import seresnet34 net = seresnet34() elif args.net == 'seresnet50': from models.senet import seresnet50 net = seresnet50() elif args.net == 'seresnet101': from models.senet import seresnet101 net = seresnet101() elif args.net == 'seresnet152': from models.senet import seresnet152 net = seresnet152() elif args.net == 'wideresnet': from models.wideresidual import wideresnet net = wideresnet() elif args.net == 'stochasticdepth18': from models.stochasticdepth import stochastic_depth_resnet18 net = stochastic_depth_resnet18() elif args.net == 'stochasticdepth34': from models.stochasticdepth import stochastic_depth_resnet34 net = stochastic_depth_resnet34() elif args.net == 'stochasticdepth50': from models.stochasticdepth import stochastic_depth_resnet50 net = stochastic_depth_resnet50() elif args.net == 'stochasticdepth101': from models.stochasticdepth import stochastic_depth_resnet101 net = stochastic_depth_resnet101() elif args.net == 'normal_resnet': from models.normal_resnet import resnet18 net = resnet18() elif args.net == 'hyper_resnet': from models.hypernet_main import Hypernet_Main net = Hypernet_Main( encoder="resnet18", hypernet_params={'vqvae_dict_size': args.dict_size}) elif args.net == 'normal_resnet_wo_bn': from models.normal_resnet_wo_bn import resnet18 net = resnet18() elif args.net == 'hyper_resnet_wo_bn': from models.hypernet_main import Hypernet_Main net = Hypernet_Main( encoder="resnet18_wobn", hypernet_params={'vqvae_dict_size': args.dict_size}) 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, use_gpu=True): """ return given network """ if args.net == 'vgg16': from models.vgg import vgg16_bn net = vgg16_bn() elif args.net == 'vgg13': from models.vgg import vgg13_bn net = vgg13_bn() elif args.net == 'vgg11': from models.vgg import vgg11_bn net = vgg11_bn() elif args.net == 'vgg19': from models.vgg import vgg19_bn net = vgg19_bn() 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() 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() elif args.net == 'resnet18': from models.resnet import resnet18 net = resnet18() elif args.net == 'resnet34': from models.resnet import resnet34 net = resnet34() elif args.net == 'resnet50': from models.resnet import resnet50 net = resnet50() elif args.net == 'resnet101': from models.resnet import resnet101 net = resnet101() elif args.net == 'resnet152': from models.resnet import resnet152 net = resnet152() elif args.net == 'preactresnet18': from models.preactresnet import preactresnet18 net = preactresnet18() elif args.net == 'preactresnet34': from models.preactresnet import preactresnet34 net = preactresnet34() elif args.net == 'preactresnet50': from models.preactresnet import preactresnet50 net = preactresnet50() elif args.net == 'preactresnet101': from models.preactresnet import preactresnet101 net = preactresnet101() elif args.net == 'preactresnet152': from models.preactresnet import preactresnet152 net = preactresnet152() elif args.net == 'resnext50': from models.resnext import resnext50 net = resnext50() elif args.net == 'resnext101': from models.resnext import resnext101 net = resnext101() elif args.net == 'resnext152': from models.resnext import resnext152 net = resnext152() 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() elif args.net == 'mobilenetv2': from models.mobilenetv2 import mobilenetv2 net = mobilenetv2() elif args.net == 'nasnet': from models.nasnet import nasnet net = nasnet() 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() elif args.net == 'seresnet34': from models.senet import seresnet34 net = seresnet34() elif args.net == 'seresnet50': from models.senet import seresnet50 net = seresnet50() elif args.net == 'seresnet101': from models.senet import seresnet101 net = seresnet101() elif args.net == 'seresnet152': from models.senet import seresnet152 net = seresnet152() else: print('the network name you have entered is not supported yet') sys.exit() if use_gpu: net = net.cuda() return net
def generate_model(opt): assert opt.model in ['c3d', 'squeezenet', 'mobilenet', 'resnext', 'resnet', 'shufflenet', 'mobilenetv2', 'shufflenetv2'] if opt.model == 'c3d': from models.c3d import get_fine_tuning_parameters model = c3d.get_model( num_classes=opt.n_classes, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model == 'squeezenet': from models.squeezenet import get_fine_tuning_parameters model = squeezenet.get_model( version=opt.version, num_classes=opt.n_classes, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model == 'shufflenet': from models.shufflenet import get_fine_tuning_parameters model = shufflenet.get_model( groups=opt.groups, width_mult=opt.width_mult, num_classes=opt.n_classes) elif opt.model == 'shufflenetv2': from models.shufflenetv2 import get_fine_tuning_parameters model = shufflenetv2.get_model( num_classes=opt.n_classes, sample_size=opt.sample_size, width_mult=opt.width_mult) elif opt.model == 'mobilenet': from models.mobilenet import get_fine_tuning_parameters model = mobilenet.get_model( num_classes=opt.n_classes, sample_size=opt.sample_size, width_mult=opt.width_mult) elif opt.model == 'mobilenetv2': from models.mobilenetv2 import get_fine_tuning_parameters model = mobilenetv2.get_model( num_classes=opt.n_classes, sample_size=opt.sample_size, width_mult=opt.width_mult) elif opt.model == 'resnext': assert opt.model_depth in [50, 101, 152] from models.resnext import get_fine_tuning_parameters if opt.model_depth == 50: model = resnext.resnext50( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, cardinality=opt.resnext_cardinality, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 101: model = resnext.resnext101( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, cardinality=opt.resnext_cardinality, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 152: model = resnext.resnext152( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, cardinality=opt.resnext_cardinality, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model == 'resnet': assert opt.model_depth in [10, 18, 34, 50, 101, 152, 200] from models.resnet import get_fine_tuning_parameters if opt.model_depth == 10: model = resnet.resnet10( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 18: model = resnet.resnet18( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 34: model = resnet.resnet34( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 50: model = resnet.resnet50( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 101: model = resnet.resnet101( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 152: model = resnet.resnet152( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 200: model = resnet.resnet200( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) if not opt.no_cuda: model = model.to(device) model = nn.DataParallel(model, device_ids=None) pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad) print("Total number of trainable parameters: ", pytorch_total_params) if opt.pretrain_path: print('loading pretrained model {}'.format(opt.pretrain_path)) pretrain = torch.load(opt.pretrain_path, map_location=torch.device('cpu')) assert opt.arch == pretrain['arch'] model.load_state_dict(pretrain['state_dict']) if opt.model in ['mobilenet', 'mobilenetv2', 'shufflenet', 'shufflenetv2']: model.module.classifier = nn.Sequential( nn.Dropout(0.9), nn.Linear(model.module.classifier[1].in_features, opt.n_finetune_classes)) model.module.classifier = model.module.classifier.cuda() elif opt.model == 'squeezenet': model.module.classifier = nn.Sequential( nn.Dropout(p=0.5), nn.Conv3d(model.module.classifier[1].in_channels, opt.n_finetune_classes, kernel_size=1), nn.ReLU(inplace=True), nn.AvgPool3d((1,4,4), stride=1)) model.module.classifier = model.module.classifier.to(device) else: model.module.fc = nn.Linear(model.module.fc.in_features, opt.n_finetune_classes) model.module.fc = model.module.fc.cuda() parameters = get_fine_tuning_parameters(model, opt.ft_portion) return model, parameters else: if opt.pretrain_path: print('loading pretrained model {}'.format(opt.pretrain_path)) pretrain = torch.load(opt.pretrain_path) assert opt.arch == pretrain['arch'] model.load_state_dict(pretrain['state_dict']) if opt.model in ['mobilenet', 'mobilenetv2', 'shufflenet', 'shufflenetv2']: model.module.classifier = nn.Sequential( nn.Dropout(0.9), nn.Linear(model.module.classifier[1].in_features, opt.n_finetune_classes) ) elif opt.model == 'squeezenet': model.module.classifier = nn.Sequential( nn.Dropout(p=0.5), nn.Conv3d(model.module.classifier[1].in_channels, opt.n_finetune_classes, kernel_size=1), nn.ReLU(inplace=True), nn.AvgPool3d((1,4,4), stride=1)) else: model.module.fc = nn.Linear(model.module.fc.in_features, opt.n_finetune_classes) parameters = get_fine_tuning_parameters(model, opt.ft_begin_index) return model, parameters return model, model.parameters()
def get_network(args, use_gpu=True): """ return given network """ if args.net == 'mobilenet': from models.mobilenet import mobilenet net = mobilenet(args) elif args.net == 'mobilenetv2': from models.mobilenetv2 import mobilenetv2 net = mobilenetv2(args) elif args.net == 'vgg13': from models.vgg import vgg13_bn net = vgg13_bn(args) elif args.net == 'vgg11': from models.vgg import vgg11_bn net = vgg11_bn(args) elif args.net == 'vgg19': # from models.vgg import vgg19_bn # net = vgg19_bn(args) from torchvision.models import vgg19_bn import torch.nn as nn net = vgg19_bn(pretrained=True) net.classifier[6] = nn.Linear(4096, args.nc) elif args.net == 'densenet121': from models.densenet import densenet121 net = densenet121(args) elif args.net == 'densenet161': from models.densenet import densenet161 net = densenet161(args) elif args.net == 'densenet169': from models.densenet import densenet169 net = densenet169(args) elif args.net == 'densenet201': from models.densenet import densenet201 net = densenet201(args) elif args.net == 'googlenet': from models.googlenet import googlenet net = googlenet(args) elif args.net == 'inceptionv3': from models.inceptionv3 import inceptionv3 net = inceptionv3(args) elif args.net == 'inceptionv4': from models.inceptionv4 import inceptionv4 net = inceptionv4(args) elif args.net == 'inceptionresnetv2': from models.inceptionv4 import inception_resnet_v2 net = inception_resnet_v2(args) elif args.net == 'xception': from models.xception import xception net = xception(args) elif args.net == 'resnet18': # from models.resnet import resnet18 # net = resnet18(args) from torchvision.models import resnet18 import torch.nn as nn net = resnet18(pretrained=True) net.fc = nn.Linear(512, args.nc) elif args.net == 'resnet34': from models.resnet import resnet34 net = resnet34(args) elif args.net == 'resnet50': from models.resnet import resnet50 net = resnet50(args) elif args.net == 'resnet101': from models.resnet import resnet101 net = resnet101(args) elif args.net == 'resnet152': from models.resnet import resnet152 net = resnet152(args) elif args.net == 'preactresnet18': from models.preactresnet import preactresnet18 net = preactresnet18(args) elif args.net == 'preactresnet34': from models.preactresnet import preactresnet34 net = preactresnet34(args) elif args.net == 'preactresnet50': from models.preactresnet import preactresnet50 net = preactresnet50(args) elif args.net == 'preactresnet101': from models.preactresnet import preactresnet101 net = preactresnet101(args) elif args.net == 'preactresnet152': from models.preactresnet import preactresnet152 net = preactresnet152(args) elif args.net == 'resnext50': from models.resnext import resnext50 net = resnext50(args) elif args.net == 'resnext101': from models.resnext import resnext101 net = resnext101(args) elif args.net == 'resnext152': from models.resnext import resnext152 net = resnext152(args) elif args.net == 'shufflenet': from models.shufflenet import shufflenet net = shufflenet(args) elif args.net == 'shufflenetv2': from models.shufflenetv2 import shufflenetv2 net = shufflenetv2(args) elif args.net == 'squeezenet': from models.squeezenet import squeezenet net = squeezenet(args) elif args.net == 'mobilenet': from models.mobilenet import mobilenet net = mobilenet(args) elif args.net == 'mobilenetv2': from models.mobilenetv2 import mobilenetv2 net = mobilenetv2(args) elif args.net == 'mobilenetv3': from models.mobilenetv3 import mobileNetv3 net = mobileNetv3(args) elif args.net == 'mobilenetv3_l': from models.mobilenetv3 import mobileNetv3 net = mobileNetv3(args, mode='large') elif args.net == 'mobilenetv3_s': from models.mobilenetv3 import mobileNetv3 net = mobileNetv3(args, mode='small') elif args.net == 'nasnet': from models.nasnet import nasnetalarge net = nasnetalarge(args) elif args.net == 'attention56': from models.attention import attention56 net = attention56(args) elif args.net == 'attention92': from models.attention import attention92 net = attention92(args) elif args.net == 'seresnet18': from models.senet import seresnet18 net = seresnet18(args) elif args.net == 'seresnet34': from models.senet import seresnet34 net = seresnet34(args) elif args.net == 'seresnet50': from models.senet import seresnet50 net = seresnet50(args) elif args.net == 'seresnet101': from models.senet import seresnet101 net = seresnet101(args) elif args.net == 'seresnet152': from models.senet import seresnet152 net = seresnet152(args) elif args.net.lower() == 'sqnxt_23_1x': from models.SqueezeNext import SqNxt_23_1x net = SqNxt_23_1x(args) elif args.net.lower() == 'sqnxt_23_1xv5': from models.SqueezeNext import SqNxt_23_1x_v5 net = SqNxt_23_1x_v5(args) elif args.net.lower() == 'sqnxt_23_2x': from models.SqueezeNext import SqNxt_23_2x net = SqNxt_23_2x(args) elif args.net.lower() == 'sqnxt_23_2xv5': from models.SqueezeNext import SqNxt_23_2x_v5 net = SqNxt_23_2x_v5(args) elif args.net.lower() == 'mnasnet': # from models.MnasNet import mnasnet # net = mnasnet(args) from models.nasnet_mobile import nasnet_Mobile net = nasnet_Mobile(args) elif args.net == 'efficientnet_b0': from models.efficientnet import efficientnet_b0 net = efficientnet_b0(args) elif args.net == 'efficientnet_b1': from models.efficientnet import efficientnet_b1 net = efficientnet_b1(args) elif args.net == 'efficientnet_b2': from models.efficientnet import efficientnet_b2 net = efficientnet_b2(args) elif args.net == 'efficientnet_b3': from models.efficientnet import efficientnet_b3 net = efficientnet_b3(args) elif args.net == 'efficientnet_b4': from models.efficientnet import efficientnet_b4 net = efficientnet_b4(args) elif args.net == 'efficientnet_b5': from models.efficientnet import efficientnet_b5 net = efficientnet_b5(args) elif args.net == 'efficientnet_b6': from models.efficientnet import efficientnet_b6 net = efficientnet_b6(args) elif args.net == 'efficientnet_b7': from models.efficientnet import efficientnet_b7 net = efficientnet_b7(args) elif args.net == 'mlp': from models.mlp import MLPClassifier net = MLPClassifier(args) elif args.net == 'alexnet': from torchvision.models import alexnet import torch.nn as nn net = alexnet(pretrained=True) net.classifier[6] = nn.Linear(4096, args.nc) elif args.net == 'lambda18': from models._lambda import LambdaResnet18 net = LambdaResnet18(num_classes=args.nc, channels=args.cs) elif args.net == 'lambda34': from models._lambda import LambdaResnet34 net = LambdaResnet34(num_classes=args.nc, channels=args.cs) elif args.net == 'lambda50': from models._lambda import LambdaResnet50 net = LambdaResnet50(num_classes=args.nc, channels=args.cs) elif args.net == 'lambda101': from models._lambda import LambdaResnet101 net = LambdaResnet101(num_classes=args.nc) elif args.net == 'lambda152': from models._lambda import LambdaResnet152 net = LambdaResnet152(num_classes=args.nc, channels=args.cs) else: print('the network name you have entered is not supported yet') sys.exit() if use_gpu: net = net.cuda() return net
cls = [ x for x in os.listdir(data_path) if os.path.isdir(os.path.join(data_path, x)) ] num_class = len(cls) models = { "vgg16": vgg.vgg16_bn(num_class), "vgg19": vgg.vgg19_bn(num_class), "densenet121": densenet.densenet121(num_class), "densenet161": densenet.densenet161(num_class), "resnet34": resnet.resnet34(num_class), "resnet50": resnet.resnet50(num_class), "resnet101": resnet.resnet101(num_class), "seresnet34": senet.seresnet34(num_class), "seresnet50": senet.seresnet50(num_class), "seresnet101": senet.seresnet101(num_class), "resnext34": resnext.resnext34(num_class), "resnext50": resnext.resnext50(num_class), "resnext101": resnext.resnext101(num_class), "shufflenet": shufflenet.shufflenet(num_class), "xception": xception.xception(num_class) } for net_name in models.keys(): writer = SummaryWriter('./runs/%s_%s/' % (ds, net_name)) model = models[net_name] logger.add('./log/%s_%s_{time}.log' % (ds, net_name), level="INFO") logger.info("net:%s\t dataset:%s\t num_class:%d" % (net_name, ds, num_class)) train() writer.close()
model_name=args.model) elif args.model == "efficientnet-b3": net = efficientnet.CEfficientNet(num_classes=num_classes, pretrained=args.pretrain, model_name=args.model) elif args.model == "efficientnet-b4": net = efficientnet.CEfficientNet(num_classes=num_classes, pretrained=args.pretrain, model_name=args.model) elif args.model == "efficientnet-b5": net = efficientnet.CEfficientNet(num_classes=num_classes, pretrained=args.pretrain, model_name=args.model) elif args.model == "resnext50": net = resnext.resnext50(num_classes=num_classes) elif args.model == "googlenet": net = googlenet.GoogLeNet(num_classes=num_classes) elif args.model == "densenet": net = densenet.DenseNet_CIFAR(num_classes=num_classes) else: raise "please check model" # freeze # count = 0 # for param in net.parameters(): # count += 1 # for i, param in enumerate(net.parameters()): # if i <= count-1 - 10: # param.requires_grad = False
def get_network(args, use_gpu=True, num_train=0): """ return given network """ if args.dataset == 'cifar-10': num_classes = 10 elif args.dataset == 'cifar-100': num_classes = 100 else: num_classes = 0 if args.ignoring: if args.net == 'resnet18': from models.resnet_ign import resnet18_ign criterion = nn.CrossEntropyLoss(reduction='none') net = resnet18_ign(criterion, num_classes=num_classes, num_train=num_train,softmax=args.softmax,isalpha=args.isalpha) else: if args.net == 'vgg16': from models.vgg import vgg16_bn net = vgg16_bn() elif args.net == 'vgg13': from models.vgg import vgg13_bn net = vgg13_bn() elif args.net == 'vgg11': from models.vgg import vgg11_bn net = vgg11_bn() elif args.net == 'vgg19': from models.vgg import vgg19_bn net = vgg19_bn() 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() 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() elif args.net == 'resnet18': from models.resnet import resnet18 net = resnet18(num_classes=num_classes) elif args.net == 'resnet34': from models.resnet import resnet34 net = resnet34(num_classes=num_classes) elif args.net == 'resnet50': from models.resnet import resnet50 net = resnet50(num_classes=num_classes) elif args.net == 'resnet101': from models.resnet import resnet101 net = resnet101(num_classes=num_classes) elif args.net == 'resnet152': from models.resnet import resnet152 net = resnet152(num_classes=num_classes) elif args.net == 'preactresnet18': from models.preactresnet import preactresnet18 net = preactresnet18() elif args.net == 'preactresnet34': from models.preactresnet import preactresnet34 net = preactresnet34() elif args.net == 'preactresnet50': from models.preactresnet import preactresnet50 net = preactresnet50() elif args.net == 'preactresnet101': from models.preactresnet import preactresnet101 net = preactresnet101() elif args.net == 'preactresnet152': from models.preactresnet import preactresnet152 net = preactresnet152() elif args.net == 'resnext50': from models.resnext import resnext50 net = resnext50() elif args.net == 'resnext101': from models.resnext import resnext101 net = resnext101() elif args.net == 'resnext152': from models.resnext import resnext152 net = resnext152() 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() elif args.net == 'mobilenetv2': from models.mobilenetv2 import mobilenetv2 net = mobilenetv2() elif args.net == 'nasnet': from models.nasnet import nasnet net = nasnet() 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() elif args.net == 'seresnet34': from models.senet import seresnet34 net = seresnet34() elif args.net == 'seresnet50': from models.senet import seresnet50 net = seresnet50() elif args.net == 'seresnet101': from models.senet import seresnet101 net = seresnet101() elif args.net == 'seresnet152': from models.senet import seresnet152 net = seresnet152() else: print('the network name you have entered is not supported yet') sys.exit() if use_gpu: net = net.cuda() return net
def get_network(args): """ return given network """ if args.model == 'vgg16': from models.vgg import vgg16_bn model = vgg16_bn() elif args.model == 'vgg13': from models.vgg import vgg13_bn model = vgg13_bn() elif args.model == 'vgg11': from models.vgg import vgg11_bn model = vgg11_bn() elif args.model == 'vgg19': from models.vgg import vgg19_bn model = vgg19_bn() elif args.model == 'densenet121': from models.densenet import densenet121 model = densenet121() elif args.model == 'densenet161': from models.densenet import densenet161 model = densenet161() elif args.model == 'densenet169': from models.densenet import densenet169 model = densenet169() elif args.model == 'densenet201': from models.densenet import densenet201 model = densenet201() elif args.model == 'googlenet': from models.googlenet import googlenet model = googlenet() elif args.model == 'inceptionv3': from models.inceptionv3 import inceptionv3 model = inceptionv3() elif args.model == 'inceptionv4': from models.inceptionv4 import inceptionv4 model = inceptionv4() elif args.model == 'inceptionresnetv2': from models.inceptionv4 import inception_resnet_v2 model = inception_resnet_v2() elif args.model == 'xception': from models.xception import xception model = xception() elif args.model == 'resnet18': from models.resnet import resnet18 model = resnet18() elif args.model == 'resnet34': from models.resnet import resnet34 model = resnet34() elif args.model == 'resnet50': from models.resnet import resnet50 model = resnet50() elif args.model == 'resnet101': from models.resnet import resnet101 model = resnet101() elif args.model == 'resnet152': from models.resnet import resnet152 model = resnet152() elif args.model == 'preactresnet18': from models.preactresnet import preactresnet18 model = preactresnet18() elif args.model == 'preactresnet34': from models.preactresnet import preactresnet34 model = preactresnet34() elif args.model == 'preactresnet50': from models.preactresnet import preactresnet50 model = preactresnet50() elif args.model == 'preactresnet101': from models.preactresnet import preactresnet101 model = preactresnet101() elif args.model == 'preactresnet152': from models.preactresnet import preactresnet152 model = preactresnet152() elif args.model == 'resnext50': from models.resnext import resnext50 model = resnext50() elif args.model == 'resnext101': from models.resnext import resnext101 model = resnext101() elif args.model == 'resnext152': from models.resnext import resnext152 model = resnext152() elif args.model == 'shufflenet': from models.shufflenet import shufflenet model = shufflenet() elif args.model == 'shufflenetv2': from models.shufflenetv2 import shufflenetv2 model = shufflenetv2() elif args.model == 'squeezenet': from models.squeezenet import squeezenet model = squeezenet() elif args.model == 'mobilenet': from models.mobilenet import mobilenet model = mobilenet() elif args.model == 'mobilenetv2': from models.mobilenetv2 import mobilenetv2 model = mobilenetv2() elif args.model == 'nasnet': from models.nasnet import nasnet model = nasnet() elif args.model == 'attention56': from models.attention import attention56 model = attention56() elif args.model == 'attention92': from models.attention import attention92 model = attention92() elif args.model == 'seresnet18': from models.senet import seresnet18 model = seresnet18() elif args.model == 'seresnet34': from models.senet import seresnet34 model = seresnet34() elif args.model == 'seresnet50': from models.senet import seresnet50 model = seresnet50() elif args.model == 'seresnet101': from models.senet import seresnet101 model = seresnet101() elif args.model == 'seresnet152': from models.senet import seresnet152 model = seresnet152() elif args.model == 'wideresnet': from models.wideresidual import wideresnet model = wideresnet() elif args.model == 'stochasticdepth18': from models.stochasticdepth import stochastic_depth_resnet18 model = stochastic_depth_resnet18() elif args.model == 'stochasticdepth34': from models.stochasticdepth import stochastic_depth_resnet34 model = stochastic_depth_resnet34() elif args.model == 'stochasticdepth50': from models.stochasticdepth import stochastic_depth_resnet50 model = stochastic_depth_resnet50() elif args.model == 'stochasticdepth101': from models.stochasticdepth import stochastic_depth_resnet101 model = stochastic_depth_resnet101() else: print('the network name you have entered is not supported yet') sys.exit() return model
def get_model(args): if args.datasets == 'ImageNet': return models_imagenet.__dict__[args.arch]() if args.datasets == 'CIFAR10' or args.datasets == 'MNIST': num_class = 10 elif args.datasets == 'CIFAR100': num_class = 100 if args.datasets == 'CIFAR100': if args.arch == 'vgg16': from models.vgg import vgg16_bn net = vgg16_bn() elif args.arch == 'vgg13': from models.vgg import vgg13_bn net = vgg13_bn() elif args.arch == 'vgg11': from models.vgg import vgg11_bn net = vgg11_bn() elif args.arch == 'vgg19': from models.vgg import vgg19_bn net = vgg19_bn() elif args.arch == 'densenet121': from models.densenet import densenet121 net = densenet121() elif args.arch == 'densenet161': from models.densenet import densenet161 net = densenet161() elif args.arch == 'densenet169': from models.densenet import densenet169 net = densenet169() elif args.arch == 'densenet201': from models.densenet import densenet201 net = densenet201() elif args.arch == 'googlenet': from models.googlenet import googlenet net = googlenet() elif args.arch == 'inceptionv3': from models.inceptionv3 import inceptionv3 net = inceptionv3() elif args.arch == 'inceptionv4': from models.inceptionv4 import inceptionv4 net = inceptionv4() elif args.arch == 'inceptionresnetv2': from models.inceptionv4 import inception_resnet_v2 net = inception_resnet_v2() elif args.arch == 'xception': from models.xception import xception net = xception() elif args.arch == 'resnet18': from models.resnet import resnet18 net = resnet18() elif args.arch == 'resnet34': from models.resnet import resnet34 net = resnet34() elif args.arch == 'resnet50': from models.resnet import resnet50 net = resnet50() elif args.arch == 'resnet101': from models.resnet import resnet101 net = resnet101() elif args.arch == 'resnet152': from models.resnet import resnet152 net = resnet152() elif args.arch == 'preactresnet18': from models.preactresnet import preactresnet18 net = preactresnet18() elif args.arch == 'preactresnet34': from models.preactresnet import preactresnet34 net = preactresnet34() elif args.arch == 'preactresnet50': from models.preactresnet import preactresnet50 net = preactresnet50() elif args.arch == 'preactresnet101': from models.preactresnet import preactresnet101 net = preactresnet101() elif args.arch == 'preactresnet152': from models.preactresnet import preactresnet152 net = preactresnet152() elif args.arch == 'resnext50': from models.resnext import resnext50 net = resnext50() elif args.arch == 'resnext101': from models.resnext import resnext101 net = resnext101() elif args.arch == 'resnext152': from models.resnext import resnext152 net = resnext152() elif args.arch == 'shufflenet': from models.shufflenet import shufflenet net = shufflenet() elif args.arch == 'shufflenetv2': from models.shufflenetv2 import shufflenetv2 net = shufflenetv2() elif args.arch == 'squeezenet': from models.squeezenet import squeezenet net = squeezenet() elif args.arch == 'mobilenet': from models.mobilenet import mobilenet net = mobilenet() elif args.arch == 'mobilenetv2': from models.mobilenetv2 import mobilenetv2 net = mobilenetv2() elif args.arch == 'nasnet': from models.nasnet import nasnet net = nasnet() elif args.arch == 'attention56': from models.attention import attention56 net = attention56() elif args.arch == 'attention92': from models.attention import attention92 net = attention92() elif args.arch == 'seresnet18': from models.senet import seresnet18 net = seresnet18() elif args.arch == 'seresnet34': from models.senet import seresnet34 net = seresnet34() elif args.arch == 'seresnet50': from models.senet import seresnet50 net = seresnet50() elif args.arch == 'seresnet101': from models.senet import seresnet101 net = seresnet101() elif args.arch == 'seresnet152': from models.senet import seresnet152 net = seresnet152() elif args.arch == 'wideresnet': from models.wideresidual import wideresnet net = wideresnet() elif args.arch == 'stochasticdepth18': from models.stochasticdepth import stochastic_depth_resnet18 net = stochastic_depth_resnet18() elif args.arch == 'efficientnet': from models.efficientnet import efficientnet net = efficientnet(1, 1, 100, bn_momentum=0.9) elif args.arch == 'stochasticdepth34': from models.stochasticdepth import stochastic_depth_resnet34 net = stochastic_depth_resnet34() elif args.arch == 'stochasticdepth50': from models.stochasticdepth import stochastic_depth_resnet50 net = stochastic_depth_resnet50() elif args.arch == 'stochasticdepth101': from models.stochasticdepth import stochastic_depth_resnet101 net = stochastic_depth_resnet101() else: net = resnet.__dict__[args.arch](num_classes=num_class) return net return resnet.__dict__[args.arch](num_classes=num_class)
def get_network(args): """ return given network """ if args.net == 'vgg16': from models.vgg import vgg16_bn net = vgg16_bn() elif args.net == 'vgg13': from models.vgg import vgg13_bn net = vgg13_bn() elif args.net == 'vgg11': from models.vgg import vgg11_bn net = vgg11_bn() elif args.net == 'vgg19': from models.vgg import vgg19_bn net = vgg19_bn() # elif args.net == 'efficientnet': # from models.effnetv2 import effnetv2_s # net = effnetv2_s() 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() 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() elif args.net == 'resnet18': from models.resnet import resnet18 net = resnet18() elif args.net == 'resnet34': from models.resnet import resnet34 net = resnet34() elif args.net == 'resnet50': from models.resnet import resnet50 net = resnet50() elif args.net == 'resnet101': from models.resnet import resnet101 net = resnet101() elif args.net == 'resnet152': from models.resnet import resnet152 net = resnet152() elif args.net == 'preactresnet18': from models.preactresnet import preactresnet18 net = preactresnet18() elif args.net == 'preactresnet34': from models.preactresnet import preactresnet34 net = preactresnet34() elif args.net == 'preactresnet50': from models.preactresnet import preactresnet50 net = preactresnet50() elif args.net == 'preactresnet101': from models.preactresnet import preactresnet101 net = preactresnet101() elif args.net == 'preactresnet152': from models.preactresnet import preactresnet152 net = preactresnet152() elif args.net == 'resnext50': from models.resnext import resnext50 net = resnext50() elif args.net == 'resnext101': from models.resnext import resnext101 net = resnext101() elif args.net == 'resnext152': from models.resnext import resnext152 net = resnext152() 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() elif args.net == 'mobilenetv2': from models.mobilenetv2 import mobilenetv2 net = mobilenetv2() elif args.net == 'nasnet': from models.nasnet import nasnet net = nasnet() 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() elif args.net == 'seresnet34': from models.senet import seresnet34 net = seresnet34() elif args.net == 'seresnet50': from models.senet import seresnet50 net = seresnet50() elif args.net == 'seresnet101': from models.senet import seresnet101 net = seresnet101() elif args.net == 'seresnet152': from models.senet import seresnet152 net = seresnet152() elif args.net == 'wideresnet': from models.wideresidual import wideresnet net = wideresnet() elif args.net == 'stochasticdepth18': from models.stochasticdepth import stochastic_depth_resnet18 net = stochastic_depth_resnet18() elif args.net == 'stochasticdepth34': from models.stochasticdepth import stochastic_depth_resnet34 net = stochastic_depth_resnet34() elif args.net == 'stochasticdepth50': from models.stochasticdepth import stochastic_depth_resnet50 net = stochastic_depth_resnet50() elif args.net == 'stochasticdepth101': from models.stochasticdepth import stochastic_depth_resnet101 net = stochastic_depth_resnet101() elif args.net == 'efficientnetb0': from models.efficientnet import efficientnetb0 net = efficientnetb0() elif args.net == 'efficientnetb1': from models.efficientnet import efficientnetb1 net = efficientnetb1() elif args.net == 'efficientnetb2': from models.efficientnet import efficientnetb2 net = efficientnetb2() elif args.net == 'efficientnetb3': from models.efficientnet import efficientnetb3 net = efficientnetb3() elif args.net == 'efficientnetb4': from models.efficientnet import efficientnetb4 net = efficientnetb4() elif args.net == 'efficientnetb5': from models.efficientnet import efficientnetb5 net = efficientnetb5() elif args.net == 'efficientnetb6': from models.efficientnet import efficientnetb6 net = efficientnetb6() elif args.net == 'efficientnetb7': from models.efficientnet import efficientnetb7 net = efficientnetb7() elif args.net == 'efficientnetl2': from models.efficientnet import efficientnetl2 net = efficientnetl2() elif args.net == 'eff': from models.efficientnet_pytorch import EfficientNet net = EfficientNet.from_pretrained('efficientnet-b7', num_classes=2) else: print('the network name you have entered is not supported yet') sys.exit() if args.gpu: #use_gpu net = net.cuda() print("use-gpu") 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 == '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
def generate_model(opt): assert opt.model in ['c3d', 'squeezenet', 'mobilenet', 'resnext', 'resnet', 'resnetl', 'shufflenet', 'mobilenetv2', 'shufflenetv2'] if opt.model == 'resnetl': assert opt.model_depth in [10] # 깊이는 10만 된다! from models.resnetl import get_fine_tuning_parameters # 전이학습을 위함. if opt.model_depth == 10: model = resnetl.resnetl10( num_classes=opt.n_classes, # 클래스 개수. shortcut_type=opt.resnet_shortcut, # 디폴트 값 : 'B' sample_size=opt.sample_size, # 디폴트 값 : 112 sample_duration=opt.sample_duration) # 디폴트 값 : 16 , 입력 프레임 elif opt.model == 'resnext': assert opt.model_depth in [50, 101, 152] from models.resnext import get_fine_tuning_parameters if opt.model_depth == 50: model = resnext.resnext50( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, cardinality=opt.resnext_cardinality, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 101: model = resnext.resnext101( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, cardinality=opt.resnext_cardinality, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 152: model = resnext.resnext152( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, cardinality=opt.resnext_cardinality, sample_size=opt.sample_size, sample_duration=opt.sample_duration) if not opt.no_cuda: if opt.gpus == '0': model = model.cuda() else: opt.gpus = opt.local_rank torch.cuda.set_device(opt.gpus) model = model.cuda() #model = nn.DataParallel(model, device_ids=None) # 병렬처리를 위함인데, 안쓸 것 같음. pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad) # grad를 하는 파라미터들을 모두 더한다. print("Total number of trainable parameters: ", pytorch_total_params) # 파라미터 값 출력 if opt.pretrain_path: # 전이학습. print('loading pretrained model {}'.format(opt.pretrain_path)) pretrain = torch.load(opt.pretrain_path, map_location=torch.device('cpu')) # print(opt.arch) # print(pretrain['arch']) # assert opt.arch == pretrain['arch'] model = modify_kernels(opt, model, opt.pretrain_modality) model.load_state_dict(pretrain['state_dict']) if opt.model in ['mobilenet', 'mobilenetv2', 'shufflenet', 'shufflenetv2']: model.module.classifier = nn.Sequential( nn.Dropout(0.5), nn.Linear(model.module.classifier[1].in_features, opt.n_finetune_classes)) model.module.classifier = model.module.classifier.cuda() elif opt.model == 'squeezenet': model.module.classifier = nn.Sequential( nn.Dropout(p=0.5), nn.Conv3d(model.module.classifier[1].in_channels, opt.n_finetune_classes, kernel_size=1), nn.ReLU(inplace=True), nn.AvgPool3d((1,4,4), stride=1)) model.module.classifier = model.module.classifier.cuda() else: model.module.fc = nn.Linear(model.module.fc.in_features, opt.n_finetune_classes) model.module.fc = model.module.fc.cuda() model = modify_kernels(opt, model, opt.modality) else: # 전이학습이 아닐때 pass model = modify_kernels(opt, model, opt.modality) parameters = get_fine_tuning_parameters(model, opt.ft_portion) # 전이학습할때만 적용 지금은 그냥 파라미터 그대로 반환됨. return model, parameters
def generate_model(opt): assert opt.model in ['xcresnet', 'resnet', 'resnext', 'i6f_resnet'] if opt.model == 'xcresnet': assert opt.model_depth in [10, 18, 34, 50, 101, 152] from models.x_channel_resnet import get_fine_tuning_parameters if opt.model_depth == 10: model = x_channel_resnet.xcresnet10(num_classes=opt.n_classes, image_nums=opt.sample_duration) elif opt.model_depth == 18: model = x_channel_resnet.xcresnet18(num_classes=opt.n_classes, image_nums=opt.sample_duration) elif opt.model_depth == 34: model = x_channel_resnet.xcresnet34(num_classes=opt.n_classes, image_nums=opt.sample_duration) elif opt.model_depth == 50: model = x_channel_resnet.xcresnet50(num_classes=opt.n_classes, image_nums=opt.sample_duration) elif opt.model_depth == 101: model = x_channel_resnet.xcresnet101( num_classes=opt.n_classes, image_nums=opt.sample_duration) elif opt.model_depth == 152: model = x_channel_resnet.xcresnet152( num_classes=opt.n_classes, image_nums=opt.sample_duration) elif opt.model == 'resnet': assert opt.model_depth in [10, 18, 34, 50, 101, 152, 200] from models.resnet import get_fine_tuning_parameters if opt.model_depth == 10: model = resnet.resnet10(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 18: model = resnet.resnet18(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 34: model = resnet.resnet34(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 50: model = resnet.resnet50(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 101: model = resnet.resnet101(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 152: model = resnet.resnet152(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 200: model = resnet.resnet200(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model == 'resnext': assert opt.model_depth in [50, 101, 152] from models.resnext import get_fine_tuning_parameters if opt.model_depth == 50: model = resnext.resnext50(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, cardinality=opt.resnext_cardinality, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 101: model = resnext.resnext101(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, cardinality=opt.resnext_cardinality, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 152: model = resnext.resnext152(num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, cardinality=opt.resnext_cardinality, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model == 'i6f_resnet': assert opt.model_depth in [10, 18, 34, 50, 101, 152, 200] #from models.resnet import get_fine_tuning_parameters if opt.model_depth == 10: model = i6f_resnet.i6f_resnet10( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 18: model = i6f_resnet.i6f_resnet18( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 34: model = i6f_resnet.i6f_resnet34( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 50: model = i6f_resnet.i6f_resnet50( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 101: model = i6f_resnet.i6f_resnet101( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 152: model = i6f_resnet.i6f_resnet152( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) elif opt.model_depth == 200: model = i6f_resnet.i6f_resnet200( num_classes=opt.n_classes, shortcut_type=opt.resnet_shortcut, sample_size=opt.sample_size, sample_duration=opt.sample_duration) if not opt.no_cuda: model = model.cuda() #model = nn.DataParallel(model,device_ids=None) if opt.pretrain_path: print('loading pretrained model {}'.format(opt.pretrain_path)) pretrain = torch.load(opt.pretrain_path) assert opt.arch == pretrain['arch'] model.load_state_dict(pretrain['state_dict']) model.fc = nn.Linear(model.fc.in_features, opt.n_finetune_classes) model.fc = model.fc.cuda() parameters = get_fine_tuning_parameters(model, opt.ft_begin_index) model = nn.DataParallel(model, device_ids=None) return model, parameters else: if opt.pretrain_path: print('loading pretrained model {}'.format(opt.pretrain_path)) pretrain = torch.load(opt.pretrain_path) assert opt.arch == pretrain['arch'] model.load_state_dict(pretrain['state_dict']) modele.fc = nn.Linear(model.module.fc.in_features, opt.n_finetune_classes) parameters = get_fine_tuning_parameters(model, opt.ft_begin_index) return model, parameters model = nn.DataParallel(model, device_ids=None) return model, model.parameters()