def __init__(self,
upper_part_list=[1, 2, 3, 4],
lower_part_list=[5, 6],
in_dim=256,
hidden_dim=10,
cls_p=7,
cls_h=3,
cls_f=2):
super(Half_Graph, self).__init__()
self.upper_part_list = upper_part_list
self.lower_part_list = lower_part_list
self.upper_parts_len = len(upper_part_list)
self.lower_parts_len = len(lower_part_list)
self.hidden = hidden_dim
self.decomp_fhu = Decomposition(in_dim, hidden_dim)
self.decomp_fhl = Decomposition(in_dim, hidden_dim)
self.part_dp_h = Part_Dependency(in_dim, hidden_dim)
self.part_dp_l = Part_Dependency(in_dim, hidden_dim)
self.att = nn.Sequential(
nn.Conv2d((cls_h - 1) * hidden_dim,
cls_h - 1,
kernel_size=1,
padding=0,
stride=1,
bias=True,
groups=cls_h - 1), nn.Sigmoid())
self.comp_phu = Composition(hidden_dim, self.upper_parts_len)
self.comp_phl = Composition(hidden_dim, self.lower_parts_len)
self.update_u = conv_Update(hidden_dim)
self.update_l = conv_Update(hidden_dim)
def __init__(self,
upper_part_list=[1, 2, 3, 4],
lower_part_list=[5, 6],
in_dim=256,
hidden_dim=10,
cls_p=7,
cls_h=3,
cls_f=2):
super(Half_Graph, self).__init__()
self.upper_part_list = upper_part_list
self.lower_part_list = lower_part_list
self.upper_parts_len = len(upper_part_list)
self.lower_parts_len = len(lower_part_list)
self.hidden = hidden_dim
self.decomp_fhu = Decomposition(hidden_dim)
self.comp_phu = Composition(hidden_dim, self.upper_parts_len)
self.part_dp_u = Part_Dependency(hidden_dim)
self.part_dp_l = Part_Dependency(hidden_dim)
self.decomp_fhl = Decomposition(hidden_dim)
self.comp_phl = Composition(hidden_dim, self.lower_parts_len)
self.update_u = conv_Update(hidden_dim, 3)
self.update_l = conv_Update(hidden_dim, 3)
def __init__(self,
adj_matrix,
upper_part_list=[1, 2, 3, 4],
lower_part_list=[5, 6],
in_dim=256,
hidden_dim=10,
cls_p=7,
cls_h=3,
cls_f=2):
super(Part_Graph, self).__init__()
self.cls_p = cls_p
self.upper_part_list = upper_part_list
self.lower_part_list = lower_part_list
self.edge_index = torch.nonzero(adj_matrix)
self.edge_index_num = self.edge_index.shape[0]
self.part_dp_list = nn.ModuleList(
[Part_Dependency(hidden_dim) for i in range(self.edge_index_num)])
self.decomp_hp_list = nn.ModuleList(
[Decomposition(hidden_dim) for i in range(cls_p - 1)])
self.decomp_fp_list = nn.ModuleList(
[Decomposition(hidden_dim) for i in range(cls_p - 1)])
self.update_conv_list = nn.ModuleList(
[conv_Update(hidden_dim, 3) for i in range(cls_p - 1)])
def __init__(self,
adj_matrix,
upper_part_list=[1, 2, 3, 4],
lower_part_list=[5, 6],
in_dim=256,
hidden_dim=10,
cls_p=7,
cls_h=3,
cls_f=2):
super(Part_Graph, self).__init__()
self.cls_p = cls_p
self.upper_part_list = upper_part_list
self.lower_part_list = lower_part_list
self.edge_index = torch.nonzero(adj_matrix)
self.edge_index_num = self.edge_index.shape[0]
self.part_dp_list = nn.ModuleList(
[Part_Dependency(hidden_dim) for i in range(self.edge_index_num)])
self.att = nn.Sequential(
nn.Conv2d((cls_p - 1) * hidden_dim,
cls_p - 1,
kernel_size=1,
padding=0,
stride=1,
bias=True,
groups=cls_p - 1), nn.Sigmoid())
self.decomp_hp_list = nn.ModuleList(
[Decomposition(hidden_dim) for i in range(cls_p - 1)])
self.decomp_fp_list = nn.ModuleList(
[Decomposition(hidden_dim) for i in range(cls_p - 1)])
self.update_conv_list = nn.ModuleList(
[conv_Update(hidden_dim, 3) for i in range(cls_p - 1)])