def __init__(self, adj, adj_mutual, person_num, hid_dim, coords_dim=(2, 3), num_layers=4, nodes_group=None):
super(MultiSemGCN, self).__init__()
_gconv_input = [_GraphConv(adj, coords_dim[0], hid_dim, p_dropout=None)] #出来是17*hid_dim的矩阵
_gconv_layers = []
_gconv_mutual = []
self.num_layers = num_layers
if nodes_group is None:
for i in range(num_layers):
_gconv_layers.append(_ResGraphConv(adj, hid_dim, hid_dim, hid_dim, p_dropout=None))
else:
group_size = len(nodes_group[0])
assert group_size > 1
grouped_order = list(reduce(lambda x, y: x + y, nodes_group)) ##list展开
restored_order = [0] * len(grouped_order)
for i in range(len(restored_order)):
for j in range(len(grouped_order)):
if grouped_order[j] == i:
restored_order[i] = j
break
## 相当于先转换为grouped_order的顺序into nonlocal layer，然后再用restored_order恢复原来的顺序
_gconv_input.append(_MutualGraphNonLocal(hid_dim, grouped_order, restored_order, group_size, person_num))
_gconv_layers.append(_ResGraphConv(adj, hid_dim, hid_dim, hid_dim, p_dropout=None))
_gconv_layers.append(_MutualGraphNonLocal(hid_dim, grouped_order, restored_order, group_size, person_num))
_gconv_mutual.append(_MultiGraphConv(person_num, adj_mutual, hid_dim, hid_dim))
self.gconv_input = nn.Sequential(*_gconv_input)
self.gconv_layers = nn.Sequential(*_gconv_layers)
self.gconv_mutual = nn.Sequential(*_gconv_mutual)
self.gconv_output = SemGraphConv(hid_dim, coords_dim[1], adj)
def __init__(self, adj, hid_dim, coords_dim=(2, 3), num_layers=4, nodes_group=None, p_dropout=None):
super(SemGCN, self).__init__()
_gconv_input = [_GraphConv(adj, coords_dim[0], hid_dim, p_dropout=p_dropout)]
_gconv_layers = []
if nodes_group is None:
for i in range(num_layers):
_gconv_layers.append(_ResGraphConv(adj, hid_dim, hid_dim, hid_dim, p_dropout=p_dropout))
else:
group_size = len(nodes_group[0])
assert group_size > 1
grouped_order = list(reduce(lambda x, y: x + y, nodes_group))
restored_order = [0] * len(grouped_order)
for i in range(len(restored_order)):
for j in range(len(grouped_order)):
if grouped_order[j] == i:
restored_order[i] = j
break
_gconv_input.append(_GraphNonLocal(hid_dim, grouped_order, restored_order, group_size))
for i in range(num_layers):
_gconv_layers.append(_ResGraphConv(adj, hid_dim, hid_dim, hid_dim, p_dropout=p_dropout))
_gconv_layers.append(_GraphNonLocal(hid_dim, grouped_order, restored_order, group_size))
self.gconv_input = nn.Sequential(*_gconv_input)
self.gconv_layers = nn.Sequential(*_gconv_layers)
self.gconv_output = SemGraphConv(hid_dim, coords_dim[1], adj)
self.LSTM = nn.LSTM()
def __init__(self, convm, adj, input_dim, output_dim, p_dropout=None):
super(_GraphConv, self).__init__()
self.gconv = SemGraphConv(convm, input_dim, output_dim, adj)
self.bn = nn.BatchNorm1d(output_dim)
self.relu = nn.ReLU()
if p_dropout is not None:
self.dropout = nn.Dropout(p_dropout)
else:
self.dropout = None
