def __init__(self,
opt,
kernel_size=2,
n_blocks=1,
state_dim_bottleneck=64,
annotation_dim_bottleneck=64):
super(ST_blocks, self).__init__()
assert opt.L - 2 * n_blocks * (kernel_size -
1) >= 1, 'L length is insufficient'
self.kernel_size = kernel_size
self.n_blocks = n_blocks
# bottleneck strategy (ST_blocks have residual connection)
self.state_dim_bottleneck = state_dim_bottleneck
self.annotation_dim_bottleneck = annotation_dim_bottleneck
self.state_dim = opt.state_dim
self.annotation_dim = opt.annotation_dim
opt.state_dim = self.state_dim_bottleneck
opt.annotation_dim = self.annotation_dim_bottleneck
for i in range(self.n_blocks):
gcnn_tp = GatedCNN(opt,
in_channels=self.state_dim,
out_channels=self.state_dim_bottleneck,
kernel_size=self.kernel_size)
#gcnn_ta = GatedCNN(opt, in_channels=self.annotation_dim, out_channels=self.annotation_dim_bottleneck, kernel_size=self.kernel_size)
opt.L = opt.L - kernel_size + 1
gcn = GCN(opt)
gcnn_bp = GatedCNN(opt,
in_channels=self.state_dim_bottleneck,
out_channels=self.state_dim,
kernel_size=self.kernel_size)
#gcnn_ba = GatedCNN(opt, in_channels=self.annotation_dim_bottleneck, out_channels=self.annotation_dim, kernel_size=self.kernel_size)
opt.L = opt.L - kernel_size + 1
self.add_module("gcnn_tp_{}".format(i), gcnn_tp)
#self.add_module("gcnn_ta_{}".format(i), gcnn_ta)
self.add_module("gcn_{}".format(i), gcn)
self.add_module("gcnn_bp_{}".format(i), gcnn_bp)
#self.add_module("gcnn_ba_{}".format(i), gcnn_ba)
opt.L = opt.init_L
opt.state_dim = self.state_dim
opt.annotation_dim = self.annotation_dim
self.gcnn_tps = AttrProxy(self, "gcnn_tp_")
#self.gcnn_tas = AttrProxy(self, "gcnn_ta_")
self.gcns = AttrProxy(self, "gcn_")
self.gcnn_bps = AttrProxy(self, "gcnn_bp_")
def __init__(self,
opt,
kernel_size=2,
n_blocks=1,
state_dim_bottleneck=64,
annotation_dim_bottleneck=64):
super(STGCN, self).__init__()
self.batchSize = opt.batchSize
self.state_dim = opt.state_dim
self.n_node = opt.n_node
# ST-Block層
self.st_blocks = ST_blocks(
opt,
kernel_size=kernel_size,
n_blocks=n_blocks,
state_dim_bottleneck=state_dim_bottleneck,
annotation_dim_bottleneck=annotation_dim_bottleneck)
opt.L = opt.L - 2 * n_blocks * (kernel_size - 1)
# 出力層 GCNN (時間軸を単一ステップへマッピング)
self.gcnn = GatedCNN(opt,
in_channels=self.state_dim,
out_channels=self.state_dim,
kernel_size=opt.L)
opt.L = 1
# 出力層 FC (回帰用)
self.out = nn.Sequential(nn.Linear(self.state_dim, opt.output_dim), )
opt.L = opt.init_L
self._initialization()
def __init__(self,
opt,
kernel_size=2,
n_blocks=1,
state_dim_bottleneck=64,
annotation_dim_bottleneck=64):
super(STGGNN, self).__init__()
self.batchSize = opt.batchSize
self.state_dim = opt.state_dim
self.n_node = opt.n_node
# ST-Block
self.st_blocks = ST_blocks(
opt,
kernel_size=kernel_size,
n_blocks=n_blocks,
state_dim_bottleneck=state_dim_bottleneck,
annotation_dim_bottleneck=annotation_dim_bottleneck)
opt.L = opt.L - 2 * n_blocks * (kernel_size - 1)
# GCNN ()
self.gcnn = GatedCNN(opt,
in_channels=self.state_dim,
out_channels=self.state_dim,
kernel_size=opt.L)
opt.L = 1
# FC ()
self.out = nn.Sequential(nn.Linear(self.n_node, self.n_node),
nn.Sigmoid())
opt.L = opt.init_L
self._initialization()
