def CreatNet(opt):
name = opt.model_name
label_num = opt.label
if name == 'lstm':
net = lstm.lstm(opt.input_size,
opt.time_step,
input_nc=opt.input_nc,
num_classes=label_num)
elif name == 'cnn_1d':
net = cnn_1d.cnn(opt.input_nc, num_classes=label_num)
elif name == 'resnet18_1d':
net = resnet_1d.resnet18()
net.conv1 = nn.Conv1d(opt.input_nc, 64, 7, 2, 3, bias=False)
net.fc = nn.Linear(512, label_num)
elif name == 'resnet34_1d':
net = resnet_1d.resnet34()
net.conv1 = nn.Conv1d(opt.input_nc, 64, 7, 2, 3, bias=False)
net.fc = nn.Linear(512, label_num)
elif name == 'multi_scale_resnet_1d':
net = multi_scale_resnet_1d.Multi_Scale_ResNet(inchannel=opt.input_nc,
num_classes=label_num)
elif name == 'micro_multi_scale_resnet_1d':
net = micro_multi_scale_resnet_1d.Multi_Scale_ResNet(
inchannel=opt.input_nc, num_classes=label_num)
elif name == 'multi_scale_resnet':
net = multi_scale_resnet.Multi_Scale_ResNet(inchannel=opt.input_nc,
num_classes=label_num)
elif name == 'dfcnn':
net = dfcnn.dfcnn(num_classes=label_num)
elif name in ['resnet101', 'resnet50', 'resnet18']:
if name == 'resnet101':
net = resnet.resnet101(pretrained=False)
net.fc = nn.Linear(2048, label_num)
elif name == 'resnet50':
net = resnet.resnet50(pretrained=False)
net.fc = nn.Linear(2048, label_num)
elif name == 'resnet18':
net = resnet.resnet18(pretrained=False)
net.fc = nn.Linear(512, label_num)
net.conv1 = nn.Conv2d(opt.input_nc, 64, 7, 2, 3, bias=False)
elif 'densenet' in name:
if name == 'densenet121':
net = densenet.densenet121(pretrained=False, num_classes=label_num)
elif name == 'densenet201':
net = densenet.densenet201(pretrained=False, num_classes=label_num)
elif name == 'squeezenet':
net = squeezenet.squeezenet1_1(pretrained=False,
num_classes=label_num,
inchannel=1)
return net
def CreatNet(name):
if name == 'lstm':
net = lstm.lstm(100, 27, num_classes=5)
elif name == 'cnn_1d':
net = cnn_1d.cnn(1, num_classes=5)
elif name == 'resnet18_1d':
net = resnet_1d.resnet18()
net.conv1 = nn.Conv1d(1, 64, 7, 2, 3, bias=False)
net.fc = nn.Linear(512, 5)
elif name == 'multi_scale_resnet_1d':
net = multi_scale_resnet_1d.Multi_Scale_ResNet(inchannel=1,
num_classes=5)
elif name == 'multi_scale_resnet':
net = multi_scale_resnet.Multi_Scale_ResNet(inchannel=1, num_classes=5)
elif name == 'dfcnn':
net = dfcnn.dfcnn(num_classes=5)
elif name in ['resnet101', 'resnet50', 'resnet18']:
if name == 'resnet101':
net = resnet.resnet101(pretrained=False)
net.fc = nn.Linear(2048, 5)
elif name == 'resnet50':
net = resnet.resnet50(pretrained=False)
net.fc = nn.Linear(2048, 5)
elif name == 'resnet18':
net = resnet.resnet18(pretrained=False)
net.fc = nn.Linear(512, 5)
net.conv1 = nn.Conv2d(1, 64, 7, 2, 3, bias=False)
elif 'densenet' in name:
if name == 'densenet121':
net = densenet.densenet121(pretrained=False, num_classes=5)
elif name == 'densenet201':
net = densenet.densenet201(pretrained=False, num_classes=5)
elif name == 'squeezenet':
net = squeezenet.squeezenet1_1(pretrained=False,
num_classes=5,
inchannel=1)
return net