import torch
from tensordict import TensorDict
import os
from os import path
import pickle
from typing import Dict, Optional
from maenvs4vrp.core.env_generator_builder import InstanceBuilder
GENERATED_INSTANCES_PATH = 'pcvrptw/data/generated'
[docs]
class ToyInstanceGenerator(InstanceBuilder):
"""
PCVRPTW toy instance generation class.
"""
[docs]
def __init__(self,
instance_type:str='validation',
set_of_instances:set=None,
device: Optional[str] = "cpu",
batch_size: Optional[torch.Size] = None,
seed:int=None) -> None:
"""
Constructor. Toy instance generator for testing.
Args:
instance_type(str): instance type. Can be "validation" or "test". Defaults to "validation".
set_of_instances(set): Set of instances file names. Defaults to None.
device(str, optional): Type of processing. It can be "cpu" or "gpu". Defaults to "cpu".
batch_size(torch.Size, optional): Batch size. If not specified, defaults to 1. Defaults to None.
seed(int): Random number generator seed. Defaults to None.
"""
# seed the generation process
if seed is None:
self._set_seed(self.DEFAULT_SEED)
else:
self._set_seed(seed)
self.device = device
if batch_size is None:
batch_size = [1]
else:
batch_size = [batch_size] if isinstance(batch_size, int) else batch_size
self.batch_size = torch.Size(batch_size)
self.max_num_agents = 4
self.max_num_nodes = 13
assert instance_type in ["validation", "test"], f"instance unknown type"
self.set_of_instances = set_of_instances
if set_of_instances:
self.instance_type = instance_type
self.load_set_of_instances()
[docs]
def random_generate_instance(self, num_agents:int=4,
num_nodes:int=13,
capacity:int=10,
profits:str='constant',
service_times:int=0.2,
batch_size:int = 1,
seed:int=None)-> TensorDict:
"""
Generate random toy instance.
Args:
num_agents(int): Total number of agents. Defaults to 4.
num_nodes(int): Total number of nodes. Defaults to 13.
capacity(int): Total capacity for each agent. Defaults to 10.
service_times(int): Service times in the nodes. Defaults to 0.2.
batch_size(int): Batch size. Defaults to 1.
seed(int, optional): Random number generator seed. Defaults to None.
Returns:
TensorDict: Instance data.
"""
if seed is not None:
self._set_seed(seed)
if num_agents is not None:
assert num_agents>0, f"number of agents must be grater them 0!"
self.max_num_agents = num_agents
if num_nodes is not None:
assert num_nodes>0, f"number of services must be grater them 0!"
self.max_num_nodes = num_nodes
if service_times is not None:
self.service_times = service_times
if capacity is not None:
assert capacity>0, f"agent capacity must be grater them 0!"
self.capacity = capacity
if batch_size is not None:
batch_size = [batch_size] if isinstance(batch_size, int) else batch_size
self.batch_size = torch.Size(batch_size)
instance = TensorDict({}, batch_size=self.batch_size, device=self.device)
self.depot_idx = 0
instance['depot_idx'] = self.depot_idx * torch.ones((*self.batch_size, 1), dtype = torch.int64, device=self.device)
coords = torch.tensor([[[0, 0],
[1, 2],
[2, 3],
[3, 2],
[-1, 2],
[-2, 3],
[-3, 2],
[-1, -2],
[-2, -3],
[-3, -2],
[1, -2],
[2, -3],
[3, -2]]], device=self.device)
instance['coords'] = coords
service_times = self.service_times * torch.ones((*self.batch_size, num_nodes), dtype = torch.float, device=self.device)
service_times[:, self.depot_idx] = 0
instance['service_time'] = service_times
time_windows = torch.tensor([[[0, 12],
[3, 6],
[4, 8],
[5, 9],
[3, 6],
[4, 8],
[5, 9],
[3, 6],
[4, 8],
[5, 9],
[3, 6],
[4, 8],
[5, 9]]], device=self.device)
instance['tw_low'] = time_windows[:, :, 0].clone()
instance['tw_high'] = time_windows[:, :, 1].clone()
instance['is_depot'] = torch.zeros((*self.batch_size, num_nodes), dtype=torch.bool, device=self.device)
instance['is_depot'][:, self.depot_idx] = True
instance['start_time'] = time_windows[:, :, 0].gather(1, torch.zeros((*self.batch_size, 1),
dtype=torch.int64, device=self.device)).squeeze(-1)
instance['end_time'] = time_windows[:, :, 1].gather(1, torch.zeros((*self.batch_size, 1),
dtype=torch.int64, device=self.device)).squeeze(-1)
demands = torch.tensor([[0., 5., 6., 4., 7., 3., 4., 6., 5., 3., 6., 5., 4.]], device=self.device)
instance['demands'] = demands
instance['capacity'] = self.capacity * torch.ones((*self.batch_size, 1), dtype = torch.float, device=self.device)
profits = torch.tensor([[0., 2., 5., 1., 2., 5., 1., 2., 5., 1., 2., 5., 1.]], device=self.device)
instance['profits'] = profits
instance_info = {'name':'toy_instance',
'num_nodes': self.max_num_nodes,
'num_agents':self.max_num_agents,
'data':instance}
return instance_info
[docs]
def sample_instance(self,
num_agents=None,
num_nodes=None,
service_times=0.2,
capacity=10,
profits:str='constant',
instance_name:str=None,
sample_type:str='random',
batch_size: Optional[torch.Size] = None,
n_augment: Optional[int] = None,
seed:int=None)-> Dict:
"""
Sample one instance from instance space.
Args:
num_agents(int): Total number of agents. Defaults to None.
num_nodes(int): Total number of nodes. Defaults to None.
service_times(float): Service times in the nodes. Defaults to 0.2.
capacity(int): Capacity of the agents. Defaults to 10.
instance_name(str): Instance name. Defaults to None.
sample_type(str): Sample type. It can be "random" or something else for "first n". Defaults to "random".
batch_size(torch.Size, optional): Batch size. Defaults to None.
n_augment(int, optional): Data augmentation. Defaults to None.
seed(int): Random number generator seed. Defaults to None.
Returns:
Dict: Instance data.
"""
if seed is not None:
self._set_seed(seed)
if self.set_of_instances is None:
random_sample = True
else:
random_sample = False
if instance_name==None and random_sample==False:
instance_name = self.sample_name_from_set(seed=seed)
elif instance_name==None and random_sample==True:
instance_name = 'random_instance'
else:
instance_name = instance_name
if num_agents is None:
num_agents = 4
if num_nodes is None:
num_nodes = 13
if service_times is None:
service_times = 0.2
if capacity is None:
capacity = 10
if batch_size is not None:
batch_size = [batch_size] if isinstance(batch_size, int) else batch_size
self.batch_size = torch.Size(batch_size)
if sample_type=='random':
instance_info = self.random_generate_instance(num_agents=num_agents,
num_nodes=num_nodes,
capacity=capacity,
profits=profits,
service_times=service_times,
batch_size = batch_size,
seed=seed)
return instance_info
if __name__ == '__main__':
number_instances = 128
print('starting valid/test sets generation')
if not os.path.exists('data/generated/test'):
os.makedirs('data/generated/test')
if not os.path.exists('data/generated/validation'):
os.makedirs('data/generated/validation')
print('done')