def __init__(self, embed_len, inst_num, c=0.05):
# for downloading pretrained model
super(MINet, self).__init__()
self.inst_num = inst_num
resnet18_pretn = models.resnet18(pretrained=True)
pretn_state_dict = resnet18_pretn.state_dict()
self.resnet18 = models.resnet18(num_classes=embed_len)
model_state_dict = self.resnet18.state_dict()
update_state = {
k: v
for k, v in pretn_state_dict.items()
if k not in ["fc.weight", "fc.bias"] and k in model_state_dict
}
model_state_dict.update(update_state)
self.resnet18.load_state_dict(model_state_dict)
self.c = c
self.tp = hypnn.ToPoincare(c=self.c,
train_x=True,
train_c=True,
ball_dim=embed_len)
self.att_weight = hypnn.HypLinear(embed_len, 1, c=self.c)
self.label_pred = hypnn.HyperbolicMLR(ball_dim=embed_len,
n_classes=2,
c=self.c)
def __init__(self, args):
super(Netold, self).__init__()
self.conv1 = nn.Conv2d(3, 20, 5, 1)
self.conv2 = nn.Conv2d(20, 50, 5, 1)
self.fc1 = nn.Linear(4 * 4 * 50, 500)
self.fc2 = nn.Linear(500, args.dim)
self.tp = hypnn.ToPoincare(
c=args.c, train_x=args.train_x, train_c=args.train_c, ball_dim=args.dim
)
self.mlr = hypnn.HyperbolicMLR(ball_dim=args.dim, n_classes=120, c=args.c)
def __init__(self, args):
super(NetHypConv2, self).__init__()
self.tp = hypnn.ToPoincare(c=args.c,
train_x=args.train_x,
train_c=args.train_c,
ball_dim=args.dim)
self.conv1 = hypnn.HypConv2(1, 20, 5, c=args.c)
self.conv2 = hypnn.HypConv2(20, 50, 5, c=args.c)
self.fc1 = hypnn.HypLinear2(4 * 4 * 50, 500, c=args.c)
self.fc2 = hypnn.HypLinear2(500, args.dim, c=args.c)
self.mlr = hypnn.HyperbolicMLR(ball_dim=args.dim,
n_classes=10,
c=args.c)
def __init__(self, embed_len, inst_num, c=0.001):
# for downloading pretrained model
super(MINet, self).__init__()
self.inst_num = inst_num
self.lenet = LeNet(embed_len)
self.c = c
self.tp = hypnn.ToPoincare(c=self.c,
train_x=True,
train_c=False,
ball_dim=embed_len)
self.att_weight = hypnn.HypLinear(embed_len, 1, c=self.c)
self.msi_pred = hypnn.HyperbolicMLR(ball_dim=embed_len,
n_classes=2,
c=self.c)
def __init__(self, args):
super(NetHypConv, self).__init__()
self.tp = hypnn.ToPoincare(c=args.c,
train_x=args.train_x,
train_c=args.train_c,
ball_dim=args.dim)
self.conv1 = hypnn.HypConv(
1, 20, 5,
c=args.c) # input: 28 x 28, output: 20 x 24 x 24 (pool 2x2 after)
self.conv2 = hypnn.HypConv(
20, 50, 5, c=args.c
) # input: 20 x 12 x 12, output: 50 x 8 x 8 (pool 2x2 after)
self.fc1 = hypnn.HypLinear(4 * 4 * 50, 500,
c=args.c) # input: 50 x 4 x 4, output: 500
self.fc2 = hypnn.HypLinear(500, args.dim, c=args.c)
self.mlr = hypnn.HyperbolicMLR(ball_dim=args.dim,
n_classes=10,
c=args.c)