def sample_many(self, input, batch_rep=1, iter_rep=1):
return sample_many(
lambda input: self._inner(*input
), # Need to unpack tuple into arguments
lambda input, pi: self.problem.get_costs(input[
0], pi), # Don't need embeddings as input to get_costs
(input, self.embeder(self.init_embed(input))
), # Pack input with embeddings (additional input)
batch_rep,
iter_rep)
def sample_many(self, input, batch_rep=1, iter_rep=1):
"""
:param input: (batch_size, graph_size, node_dim) input node features
:return:
"""
# Bit ugly but we need to pass the embeddings as well.
# Making a tuple will not work with the problem.get_cost function
return sample_many(
lambda input: self._inner(*input), # Need to unpack tuple into arguments
lambda input, pi: self.problem.get_costs(input[0], pi), # Don't need embeddings as input to get_costs
(input, self.embedder(self._init_embed(input))[0]), # Pack input with embeddings (additional input)
batch_rep, iter_rep
)
def sample_many(self, input, graph, batch_rep=1, iter_rep=1):
# Bit ugly but we need to pass the embeddings as well.
# Making a tuple will not work with the problem.get_cost function
# Params: inner_func, get_cost_func, input, batch_rep, iter_rep
return sample_many(
# Need to unpack tuple into arguments
lambda input: self._inner(*input),
# Don't need embeddings as input to get_costs
lambda input, pi: self.problem.get_costs(input[0], pi),
# Pack input with embeddings (additional input)
(input, graph, self.embedder(self._init_embed(input), graph)),
batch_rep, iter_rep
)