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import torch
import torch.nn as nn
import numpy as np
import os
import multiprocessing
from models.solvers.general_solver import GeneralSolver
from utils.utils import calc_tour_length
def get_visited_mask(tour, step, node_feats, dist_matrix=None):
"""
Visited nodes -> feasible, Unvisited nodes -> infeasible.
When solving a problem with visited_paths fixed, they should be included to the solution.
Therefore, visited nodes are set to feasible nodes.
"""
if dist_matrix is not None:
num_nodes = len(dist_matrix)
else:
num_nodes = len(node_feats["coords"])
visited = np.isin(np.arange(num_nodes), tour[:step])
return visited
def get_tw_mask(tour, step, node_feats, dist_matrix=None):
"""
Nodes whose tw exceeds current_time -> infeasible, otherwise -> feasible.
Parameters
----------
tour: list [seq_length]
step: int
node_feats: dict of np.array
Returns
-------
mask_tw: np.array [num_nodes]
"""
node_feats = node_feats.copy()
time_window = node_feats["time_window"]
if dist_matrix is not None:
num_nodes = len(dist_matrix)
curr_time = 0.0
not_exceed_tw = np.ones(num_nodes).astype(np.int32)
for i in range(1, step):
prev_id = tour[i - 1]
curr_id = tour[i]
travel_time = dist_matrix[prev_id, curr_id]
# assert curr_time + travel_time < time_window[curr_id, 1], f"Invalid tour! arrival_time: {curr_time + travel_time}, time_window: {time_window[curr_id]}"
if curr_time + travel_time < time_window[curr_id, 0]:
curr_time = time_window[curr_id, 0].copy()
else:
curr_time += travel_time
curr_time = curr_time + dist_matrix[tour[step-1]] # [num_nodes] TODO: check
else:
coords = node_feats["coords"]
num_nodes = len(coords)
curr_time = 0.0
not_exceed_tw = np.ones(num_nodes).astype(np.int32)
for i in range(1, step):
prev_id = tour[i - 1]
curr_id = tour[i]
travel_time = np.linalg.norm(coords[prev_id] - coords[curr_id])
# assert curr_time + travel_time < time_window[curr_id, 1], f"Invalid tour! arrival_time: {curr_time + travel_time}, time_window: {time_window[curr_id]}"
if curr_time + travel_time < time_window[curr_id, 0]:
curr_time = time_window[curr_id, 0].copy()
else:
curr_time += travel_time
curr_time = curr_time + np.linalg.norm(coords[tour[step-1]][None, :] - coords, axis=-1) # [num_nodes] TODO: check
not_exceed_tw[curr_time > time_window[:, 1]] = 0
not_exceed_tw = not_exceed_tw > 0
return not_exceed_tw
def get_cap_mask(tour, step, node_feats):
num_nodes = len(node_feats["coords"])
demands = node_feats["demand"]
remaining_cap = node_feats["capacity"].copy()
less_than_cap = np.ones(num_nodes).astype(np.int32)
for i in range(step):
remaining_cap -= demands[tour[i]]
less_than_cap[remaining_cap < demands] = 0
less_than_cap = less_than_cap > 0
return less_than_cap
def get_pc_mask(tour, step, node_feats):
"""
Mask for Price collecting problems (e.g., PCTSP, PCTSPTW, PCCVRP, PCCVRPTW, ...)
Returns
-------
not_exceed_max_length
"""
large_value = 1e+5
coords = node_feats["coords"]
max_length = (node_feats["max_length"] * large_value).astype(np.int64)
tour_length = 0
for i in range(1, step):
prev_id = tour[i - 1]
curr_id = tour[i]
tour_length += (np.linalg.norm(coords[prev_id] - coords[curr_id]) * large_value).astype(np.int64)
curr_to_next = (np.linalg.norm(coords[tour[step-1]][None, :] - coords, axis=-1) * large_value).astype(np.int64) # [num_nodes]
next_to_depot = (np.linalg.norm(coords[tour[0]][None, :] - coords, axis=-1) * large_value).astype(np.int64) # [num_nodes]
not_exceed_max_length = (tour_length + curr_to_next + next_to_depot) <= max_length # [num_nodes]
return not_exceed_max_length
def analyze_tour(tour, node_feats):
coords = node_feats["coords"]
time_window = node_feats["time_window"]
curr_time = 0
for i in range(1, len(tour)):
prev_id = tour[i - 1]
curr_id = tour[i]
travel_time = np.linalg.norm(coords[prev_id] - coords[curr_id])
valid = curr_time + travel_time < time_window[curr_id, 1]
print(f"visit #{i}: {prev_id} -> {curr_id}, travel_time: {travel_time}, arrival_time: {curr_time + travel_time}, time_window: {time_window[curr_id]}, valid: {valid}")
if curr_time + travel_time < time_window[curr_id, 0]:
curr_time = time_window[curr_id, 0]
else:
curr_time += travel_time
FAIL_FLAG = -1
class GroundTruthBase(nn.Module):
def __init__(self, problem, compared_problems, solver_type):
"""
Parameters
----------
"""
super().__init__()
self.problem = problem
self.compared_problems = compared_problems
self.num_compared_problems = len(compared_problems)
self.solver_type = solver_type
self.solvers = []
for i in range(self.num_compared_problems):
# TODO:
self.solvers.append(GeneralSolver(self.compared_problems[i], self.solver_type, scaling=False))
def forward(self, inputs, annotation=False, parallel=True):
"""
Parameters
----------
inputs: dict
tour: 2d list [num_vehicles x seq_length]
first_explained_step: int
node_feats: dict of np.array
annotation: bool
please set it True when annotating data
Returns
-------
labels:
probs: torch.tensor [batch_size (num_vehicles) x max_seq_length x num_classes]
"""
input_tours = inputs["tour"]
node_feats = inputs["node_feats"]
dist_matrix = inputs["dist_matrix"]
first_explained_step = inputs["first_explained_step"]
num_vehicles = len(input_tours)
if annotation:
labels = [[] for _ in range(num_vehicles)]
for vehicle_id in range(num_vehicles):
input_tour = input_tours[vehicle_id]
# analyze_tour(input_tour, node_feats)
for step in range(first_explained_step + 1, len(input_tour)):
_, __, label = self.label_path(vehicle_id, step, input_tour, node_feats)
if label == FAIL_FLAG:
return
labels[vehicle_id].append((step, label))
return labels
else:
if parallel:
labels = [[-1] * (len(range(first_explained_step+1, len(input_tours[vehicle_id])))) for vehicle_id in range(num_vehicles)]
num_cpus = os.cpu_count()
with multiprocessing.Pool(num_cpus) as pool:
for vehicle_id, step, label in pool.starmap(self.label_path, [(vehicle_id, step, input_tours[vehicle_id], node_feats, dist_matrix)
for vehicle_id in range(num_vehicles)
for step in range(first_explained_step+1, len(input_tours[vehicle_id]))]):
labels[vehicle_id][step-(first_explained_step+1)] = label
else:
labels = [[-1] * (len(range(first_explained_step+1, len(input_tours[vehicle_id])))) for vehicle_id in range(num_vehicles)]
for vehicle_id in range(num_vehicles):
for step in range(first_explained_step+1, len(input_tours[vehicle_id])):
vehicle_id, step, label = self.label_path(vehicle_id, step, input_tours[vehicle_id], node_feats, dist_matrix)
labels[vehicle_id][step-(first_explained_step+1)] = label
# validate labels
for vehicle_id in range(num_vehicles):
assert (len(input_tours[vehicle_id]) - 1) == len(labels[vehicle_id]), f"vehicle_id={vehicle_id}, {input_tours}, {labels}"
return labels
# labels = [torch.LongTensor(label) for label in labels] # [num_vehicles x seq_length]
# labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True) # [num_vehicles x max_seq_length]
# probs = torch.zeros((labels.size(0), labels.size(1), self.num_compared_problems+1)) # [num_vehicles x max_seq_length x (num_compared_problems+1)]
# probs.scatter_(-1, labels.unsqueeze(-1).expand_as(probs), 1.0)
# return probs
def label_path(self, vehicle_id, step, input_tour, node_feats, dist_matrix=None):
compared_tour_list = [[] for _ in range(self.num_compared_problems)]
visited_path = input_tour[:step].copy()
new_node_id, new_node_feats, new_dist_matrix = self.get_feasible_nodes(input_tour, step, node_feats, dist_matrix)
new_visited_path = np.array(list(map(lambda x: np.where(new_node_id==x)[0].item(), visited_path)))
for i in range(self.num_compared_problems):
# TODO: in CVRPTW / PCCVRPTW, need to modify classification of the first and last paths
compared_tours = self.solvers[i].solve(new_node_feats, new_visited_path, new_dist_matrix)
if compared_tours is None:
return vehicle_id, step, FAIL_FLAG
compared_tour = None
for compared_tour_tmp in compared_tours:
if new_visited_path[-1] in compared_tour_tmp:
compared_tour = compared_tour_tmp
break
assert compared_tour is not None, f"Found no appropriate vhiecle. {compared_tours}, {new_visited_path}"
compared_tour = np.array(list(map(lambda x: new_node_id[x], compared_tour)))
if (step > 0) and (compared_tour[1] != input_tour[1]):
compared_tour = np.flipud(compared_tour) # make direction of the cf tour the same as factual one
compared_tour_list[i] = compared_tour
# print("fixed_paths :", visited_path)
# print("input_tour :", input_tour)
# print("compared_tour:", compared_tour)
# print()
# annotation
label = self.get_label(input_tour, compared_tour_list, step)
return vehicle_id, step, label
def solve(self, step, input_tour, node_feats, instance_name=None):
compared_tours = {}
visited_path = input_tour[:step].copy()
new_node_id, new_node_feats = self.get_feasible_nodes(input_tour, step, node_feats)
new_visited_path = np.array(list(map(lambda x: np.where(new_node_id==x)[0].item(), visited_path)))
for i, compared_problem in enumerate(self.compared_problems):
compared_tours[compared_problem] = self.solvers[i].solve(new_node_feats, new_visited_path, instance_name)
compared_tours[compared_problem] = list(map(lambda compared_tour: list(map(lambda x: new_node_id[x], compared_tour)), compared_tours[compared_problem]))
compared_tours[compared_problem] = list(map(lambda compared_tour: calc_tour_length(compared_tour, node_feats["coords"]), compared_tours[compared_problem]))
return compared_tours
def get_label(self, input_tour, compared_tours, step):
for i in range(self.num_compared_problems):
compared_tour = compared_tours[i]
if input_tour[step] == compared_tour[step]:
return i
return self.num_compared_problems
def get_inputs(self, tour, first_explained_step, node_feats, dist_matrix=None):
input_features = {
"tour": tour,
"first_explained_step": first_explained_step,
"node_feats": node_feats,
"dist_matrix": dist_matrix
}
return input_features
def get_feasible_nodes(self, tour, step, node_feats, dist_matrix=None):
"""
Parameters
----------
tour: np.array [seq_length]
step: int
node_feats: np.array [num_nodes x node_dim]
Returns
-------
new_node_id: np.array [num_feasible_nodes]
new_node_feats: dict of np.array [num_feasible_nodes x coord_dim]
"""
if dist_matrix is not None:
num_nodes = len(dist_matrix)
else:
num_nodes = len(node_feats["coords"])
mask = self.get_mask(tour, step, node_feats, dist_matrix)
node_id = np.arange(num_nodes)
new_node_id = node_id[mask].copy()
new_node_feats = {
key: node_feat[mask].copy()
if key in ["coords", "time_window", "demand", "penalties", "prizes"] else
node_feat.copy()
for key, node_feat in node_feats.items()
}
if dist_matrix is not None:
delete_id = node_id[~mask]
new_dist_matrix = np.delete(np.delete(dist_matrix, delete_id, 0), delete_id, 1)
else:
new_dist_matrix = None
return new_node_id, new_node_feats, new_dist_matrix
def get_mask(self, tour, step, node_feats, dist_matrix=None):
raise NotImplementedError
def check_feasibility(self, tour, node_feats):
raise NotImplementedError |