def __init__(self, num_classes, dict_size=200, n0=2048, n1=1000, n2=500, dim=3):
super().__init__()
self.num_classes = num_classes
self.num_clusters = dict_size
self.feature_model = resnet_model('resnet50', 'layer4').cuda()
self.bin_model = bin_3layer(n0+num_classes, n1, n2, self.num_clusters).cuda()
self.res_model = res_3layer(n0+num_classes, n1, n2, dim).cuda()
def __init__(self):
super().__init__()
self.num_classes = num_classes
self.feature_model = resnet_model('resnet50', 'layer4').cuda()
self.pose_models = nn.ModuleList([
model_3layer(N0, N1, N2) for i in range(self.num_classes)
]).cuda()
def __init__(self):
super().__init__()
self.num_classes = num_classes
self.num_clusters = num_clusters
self.feature_model = resnet_model('resnet50', 'layer4').cuda()
self.bin_models = nn.ModuleList([bin_3layer(N0, N1, N2, num_clusters) for i in range(self.num_classes)]).cuda()
self.res_models = nn.ModuleList([res_2layer(N0, N3, ndim) for i in range(self.num_classes * self.num_clusters)]).cuda()
def __init__(self):
super().__init__()
self.num_classes = num_classes
self.feature_model = resnet_model('resnet50', 'layer4').cuda()
self.pose_models = nn.ModuleList([
bin_3layer(args.N0, args.N1, args.N2, num_clusters)
for i in range(self.num_classes)
]).cuda()
def __init__(self, num_classes, dict_size=16, n0=2048, n1=1000, n2=500, n3=100, dim=3):
super().__init__()
self.ndim = dim
self.num_classes = num_classes
self.num_clusters = dict_size
self.feature_model = resnet_model('resnet50', 'layer4').cuda()
self.bin_model = bin_3layer(n0+num_classes, n1, n2, self.num_clusters).cuda()
self.res_models = nn.ModuleList([res_2layer(n0+num_classes, n3, dim) for i in range(self.num_clusters)]).cuda()
def __init__(self):
super().__init__()
self.num_classes = num_classes
if args.feature_network == 'resnet':
self.feature_model = resnet_model('resnet50', 'layer4').cuda()
elif args.feature_network == 'vgg':
self.feature_model = vgg_model('vgg13', 'fc6').cuda()
self.pose_models = nn.ModuleList([model_3layer(args.N0, args.N1, args.N2, ndim) for i in range(self.num_classes)]).cuda()
def __init__(self):
super().__init__()
self.num_classes = num_classes
if args.feature_network == 'resnet':
self.feature_model = resnet_model('resnet50', 'layer4').cuda()
elif args.feature_network == 'vgg':
self.feature_model = vgg_model('vgg13', 'fc6').cuda()
self.pose_model = model_3layer(args.N0, args.N1, args.N2, ndim).cuda()
self.category_model = nn.Linear(args.N0, num_classes).cuda()
def __init__(self, feature_network, num_classes, num_clusters, N0, N1, N2,
ndim):
super().__init__()
self.num_classes = num_classes
self.num_clusters = num_clusters
self.ndim = ndim
if feature_network == 'resnet':
self.feature_model = resnet_model('resnet50', 'layer4').cuda()
elif feature_network == 'vgg':
self.feature_model = vgg_model('vgg13', 'fc6').cuda()
self.bin_models = nn.ModuleList([
bin_3layer(N0, N1, N2, num_clusters)
for i in range(self.num_classes)
]).cuda()
self.res_models = nn.ModuleList([
res_3layer(N0, N1, N2, ndim) for i in range(self.num_classes)
]).cuda()
def __init__(self, num_classes, dict_size=16, n0=2048, n1=1000, n2=500):
super().__init__()
self.num_classes = num_classes
self.feature_model = resnet_model('resnet50', 'layer4').cuda()
self.pose_model = bin_3layer(n0+num_classes, n1, n2, dict_size).cuda()
def __init__(self):
super().__init__()
self.num_classes = num_classes
self.feature_model = resnet_model('resnet50', 'layer4').cuda()
self.pose_model = model_3layer(args.N0, args.N1, args.N2, ndim).cuda()
def __init__(self):
super().__init__()
self.feature_model = resnet_model('resnet50', 'layer4').cuda()
self.fc = nn.Linear(N0, num_classes).cuda()
