def _build_graph_conv(self, F_h):
self.F_h = list(F_h) if isinstance(F_h, tuple) else F_h
self.conv, self.bn = [], []
for i, (f_in,
f_out) in enumerate(zip([self.F_e] + self.F_h[:-1], self.F_h)):
conv = modules.GraphConv(f_in, f_out, self.N_B + self.D)
self.conv.append(conv)
self.register_child(conv)
if i != 0:
bn = modules.BatchNorm(in_channels=f_in)
self.register_child(bn)
else:
bn = None
self.bn.append(bn)
self.bn_skip = modules.BatchNorm(in_channels=sum(self.F_h))
self.linear_skip = modules.Linear_BN(sum(self.F_h), self.F_skip)
# projectors for conditional variable
self.linear_c = []
for i, f_out in enumerate(self.F_h):
if self.rename:
linear_c = nn.Dense(f_out,
use_bias=False,
in_units=self.N_C,
prefix='cond_{}'.format(i))
else:
linear_c = nn.Dense(f_out, use_bias=False, in_units=self.N_C)
self.register_child(linear_c)
self.linear_c.append(linear_c)
def _build_graph_conv(self, F_h):
self.F_h = list(F_h) if isinstance(F_h, tuple) else F_h
self.conv, self.bn = [], []
for i, (f_in,
f_out) in enumerate(zip([self.F_e] + self.F_h[:-1], self.F_h)):
conv = modules.GraphConv(f_in, f_out, self.N_B + self.D)
self.conv.append(conv)
self.register_child(conv)
if i != 0:
bn = modules.BatchNorm(in_channels=f_in)
self.register_child(bn)
else:
bn = None
self.bn.append(bn)
self.bn_skip = modules.BatchNorm(in_channels=sum(self.F_h))
self.linear_skip = modules.Linear_BN(sum(self.F_h), self.F_skip)