def forward(self, s0, s1, drop_prob):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
for i in range(self._steps):
h1 = states[self._indices[2 * i]]
h2 = states[self._indices[2 * i + 1]]
op1 = self._ops[2 * i]
op2 = self._ops[2 * i + 1]
h1 = op1(h1)
h2 = op2(h2)
if self.training and drop_prob > 0.:
if not isinstance(op1, Identity):
h1 = drop_path(h1, drop_prob)
if not isinstance(op2, Identity):
h2 = drop_path(h2, drop_prob)
s = h1 + h2
states += [s]
return torch.cat([states[i] for i in self._concat], dim=1)
def forward(self, s0, s1, weights, drop_prob=0.):
s0 = self.preprocess0(s0)
s1 = self.preprocess1(s1)
states = [s0, s1]
offset = 0
for i in range(self._steps):
if drop_prob > 0. and self.training:
s = sum(
drop_path(self._ops[offset + j](h, weights[offset +
j]), drop_prob)
for j, h in enumerate(states))
else:
s = sum(self._ops[offset + j](h, weights[offset + j])
for j, h in enumerate(states))
offset += len(states)
states.append(s)
return torch.cat(states[-self._multiplier:], dim=1)