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()
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 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 ['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()