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import torch.nn as nn
from models.solvers.ortools.ortools_tsp import ORToolsTSP
from models.solvers.ortools.ortools_tsptw import ORToolsTSPTW
from models.solvers.ortools.ortools_pctsp import ORToolsPCTSP
from models.solvers.ortools.ortools_pctsptw import ORToolsPCTSPTW
from models.solvers.ortools.ortools_cvrp import ORToolsCVRP
from models.solvers.ortools.ortools_cvrptw import ORToolsCVRPTW
class ORTools(nn.Module):
def __init__(self, problem, large_value=1e+6, scaling=False):
super().__init__()
self.coord_dim = 2
self.problem = problem
self.large_value = large_value
self.scaling = scaling
self.ortools = self.get_ortools(problem)
def get_ortools(self, problem):
"""
Parameters
----------
problem: str
problem type
Returns
-------
ortools: ortools for the specified problem
"""
if problem == "tsp":
return ORToolsTSP(self.large_value, self.scaling)
elif problem == "tsptw":
return ORToolsTSPTW(self.large_value, self.scaling)
elif problem == "pctsp":
return ORToolsPCTSP(self.large_value, self.scaling)
elif problem == "pctsptw":
return ORToolsPCTSPTW(self.large_value, self.scaling)
elif problem == "cvrp":
return ORToolsCVRP(self.large_value, self.scaling)
elif problem == "cvrptw":
return ORToolsCVRPTW(self.large_value, self.scaling)
else:
raise NotImplementedError
def solve(self, node_feats, fixed_paths=None, dist_martix=None, instance_name=None):
"""
Parameters
----------
node_feats: np.array [num_nodes x node_dim]
fixed_paths: np.array [cf_step]
scaling: bool
whether or not coords are muliplied by a large value
to convert float-coods into int-coords
Returns
-------
tour: np.array [seq_length]
"""
return self.ortools.solve(node_feats, fixed_paths, dist_martix, instance_name) |