def __init__(
self,
in_dim: int,
out_dim: int,
):
super().__init__()
self.layers = nn.Sequential(
Linear(in_dim, out_dim // 2, orders=('linear', 'act')),
Linear(out_dim // 2, out_dim, orders=('linear', 'act')))
def __init__(
self,
obj_dim: int,
q_dim: int,
kernel_size: int,
obj_method: str,
):
super().__init__()
hid_dim = q_dim // 2
self.obj_method = obj_method
self.obj_prob_l = Linear(obj_dim, hid_dim, orders=('linear', 'act'))
self.q_prob_l = Linear(q_dim,
hid_dim * kernel_size,
orders=('linear', 'act'))
self.kernel_size = kernel_size
def __init__(
self,
obj_dim: int,
q_dim: int,
kernel_size: int,
obj_method: str,
):
super().__init__()
self.obj_method = obj_method
self.obj_prob_l = Linear(obj_dim, q_dim // 2, orders=('linear', 'act'))
self.q_prob_l = Linear(q_dim, q_dim // 2, orders=('linear', 'act'))
self.logit_l = nn.Sequential(
nn.Linear(self.calc_in_dim(q_dim // 2, q_dim // 2, obj_method),
q_dim // 4), nn.ReLU(inplace=True),
nn.Linear(q_dim // 4, kernel_size))
def __init__(self,
obj_dim: int,
q_dim: int,
out_dim: int,
dropout: float = None):
super().__init__()
self.obj_proj_l = Linear(obj_dim, out_dim, orders=('linear', 'act'))
self.edge_cond_l = FusionCond(obj_dim, q_dim, out_dim)
if dropout is not None:
self.drop_l = nn.Dropout(dropout)
else:
self.drop_l = Null()
def __init__(self, obj_dim: int, q_dim: int, out_dim: int):
super().__init__()
self.q_proj_l = nn.utils.weight_norm(nn.Linear(q_dim,
out_dim // 2 * 2))
self.cond_l = Linear(obj_dim,
out_dim // 2,
norm_type='layer',
norm_affine=False,
orders=('linear', 'norm', 'cond', 'act'))
self.linear_l = nn.Sequential(
nn.utils.weight_norm(nn.Linear(out_dim // 2, out_dim // 2)),
nn.ReLU(inplace=True),
nn.utils.weight_norm(nn.Linear(out_dim // 2, out_dim)))
def __init__(
self,
in_dim: int,
cond_dim: int,
out_dim: int,
norm_type='layer',
dropout: float = 0.,
):
super().__init__()
self.cond_proj_l = nn.Sequential(
nn.Dropout(dropout),
nn.utils.weight_norm(nn.Linear(cond_dim, out_dim * 2)))
self.drop_l = nn.Dropout(dropout)
self.film_l = Linear(in_dim,
out_dim // 2,
norm_type=norm_type,
norm_affine=False,
orders=('linear', 'norm', 'cond', 'act'))
def __init__(self,
node_dim: int,
cond_dim: int,
out_dim: int,
method: str = 'mean',
dropout: float = 0.):
super().__init__()
self.cond_proj_l = nn.Sequential(
nn.Dropout(dropout),
nn.utils.weight_norm(nn.Linear(cond_dim, out_dim)))
self.drop_l = nn.Dropout(dropout)
self.film_l = Linear(node_dim,
out_dim // 2,
norm_type='layer',
norm_affine=False,
orders=('linear', 'norm', 'cond', 'act'))
self.linear_l = nn.Sequential(
nn.utils.weight_norm(nn.Linear(out_dim // 2, out_dim)), )
self.relu_l = nn.ReLU()
self.method = method
def __init__(
self,
node_dim: int,
cond_dim: int,
edge_dim: int,
n2n_method: str,
dropout: float = 0.,
):
super().__init__()
self.n2n_method = n2n_method
self.node_proj_l = nn.Sequential(
nn.Dropout(dropout),
nn.utils.weight_norm(nn.Linear(node_dim, edge_dim)), nn.ReLU())
self.cond_proj_l = nn.Sequential(
nn.Dropout(dropout),
nn.utils.weight_norm(nn.Linear(cond_dim, edge_dim * 2)),
)
n_fusion_dim = edge_dim * 2 if n2n_method == 'cat' else edge_dim
self.film_l = Linear(n_fusion_dim,
edge_dim,
norm_type='layer',
norm_affine=False,
orders=('linear', 'norm', 'cond', 'act'))