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"""
Problem specific node embedding for static feature.
"""
import torch
import torch.nn as nn
def AutoEmbedding(problem_name, config):
"""
Automatically select the corresponding module according to ``problem_name``
"""
mapping = {
"tsp": TSPEmbedding,
"cvrp": VRPEmbedding,
"sdvrp": VRPEmbedding,
"pctsp": PCTSPEmbedding,
"op": OPEmbedding,
}
embeddingClass = mapping[problem_name]
embedding = embeddingClass(**config)
return embedding
class TSPEmbedding(nn.Module):
"""
Embedding for the traveling salesman problem.
Args:
embedding_dim: dimension of output
Inputs: input
* **input** : [batch, n_customer, 2]
Outputs: out
* **out** : [batch, n_customer, embedding_dim]
"""
def __init__(self, embedding_dim):
super(TSPEmbedding, self).__init__()
node_dim = 2 # x, y
self.context_dim = 2 * embedding_dim # Embedding of first and last node
self.init_embed = nn.Linear(node_dim, embedding_dim)
def forward(self, input):
out = self.init_embed(input)
return out
class VRPEmbedding(nn.Module):
"""
Embedding for the capacitated vehicle routing problem.
The shape of tensors in ``input`` is summarized as following:
+-----------+-------------------------+
| key | size of tensor |
+===========+=========================+
| 'loc' | [batch, n_customer, 2] |
+-----------+-------------------------+
| 'depot' | [batch, 2] |
+-----------+-------------------------+
| 'demand' | [batch, n_customer, 1] |
+-----------+-------------------------+
Args:
embedding_dim: dimension of output
Inputs: input
* **input** : dict of ['loc', 'depot', 'demand']
Outputs: out
* **out** : [batch, n_customer+1, embedding_dim]
"""
def __init__(self, embedding_dim):
super(VRPEmbedding, self).__init__()
node_dim = 3 # x, y, demand
self.context_dim = embedding_dim + 1 # Embedding of last node + remaining_capacity
self.init_embed = nn.Linear(node_dim, embedding_dim)
self.init_embed_depot = nn.Linear(2, embedding_dim) # depot embedding
def forward(self, input): # dict of 'loc', 'demand', 'depot'
# batch, 1, 2 -> batch, 1, embedding_dim
depot_embedding = self.init_embed_depot(input["depot"])[:, None, :]
# [batch, n_customer, 2, batch, n_customer, 1] -> batch, n_customer, embedding_dim
node_embeddings = self.init_embed(
torch.cat((input["loc"], input["demand"][:, :, None]), -1)
)
# batch, n_customer+1, embedding_dim
out = torch.cat((depot_embedding, node_embeddings), 1)
return out
class PCTSPEmbedding(nn.Module):
"""
Embedding for the prize collecting traveling salesman problem.
The shape of tensors in ``input`` is summarized as following:
+------------------------+-------------------------+
| key | size of tensor |
+========================+=========================+
| 'loc' | [batch, n_customer, 2] |
+------------------------+-------------------------+
| 'depot' | [batch, 2] |
+------------------------+-------------------------+
| 'deterministic_prize' | [batch, n_customer, 1] |
+------------------------+-------------------------+
| 'penalty' | [batch, n_customer, 1] |
+------------------------+-------------------------+
Args:
embedding_dim: dimension of output
Inputs: input
* **input** : dict of ['loc', 'depot', 'deterministic_prize', 'penalty']
Outputs: out
* **out** : [batch, n_customer+1, embedding_dim]
"""
def __init__(self, embedding_dim):
super(PCTSPEmbedding, self).__init__()
node_dim = 4 # x, y, prize, penalty
self.context_dim = embedding_dim + 1 # Embedding of last node + remaining prize to collect
self.init_embed = nn.Linear(node_dim, embedding_dim)
self.init_embed_depot = nn.Linear(2, embedding_dim) # depot embedding
def forward(self, input): # dict of 'loc', 'deterministic_prize', 'penalty', 'depot'
# batch, 1, 2 -> batch, 1, embedding_dim
depot_embedding = self.init_embed_depot(input["depot"])[:, None, :]
# [batch, n_customer, 2, batch, n_customer, 1, batch, n_customer, 1] -> batch, n_customer, embedding_dim
node_embeddings = self.init_embed(
torch.cat(
(
input["loc"],
input["deterministic_prize"][:, :, None],
input["penalty"][:, :, None],
),
-1,
)
)
# batch, n_customer+1, embedding_dim
out = torch.cat((depot_embedding, node_embeddings), 1)
return out
class OPEmbedding(nn.Module):
"""
Embedding for the orienteering problem.
The shape of tensors in ``input`` is summarized as following:
+----------+-------------------------+
| key | size of tensor |
+==========+=========================+
| 'loc' | [batch, n_customer, 2] |
+----------+-------------------------+
| 'depot' | [batch, 2] |
+----------+-------------------------+
| 'prize' | [batch, n_customer, 1] |
+----------+-------------------------+
Args:
embedding_dim: dimension of output
Inputs: input
* **input** : dict of ['loc', 'depot', 'prize']
Outputs: out
* **out** : [batch, n_customer+1, embedding_dim]
"""
def __init__(self, embedding_dim):
super(OPEmbedding, self).__init__()
node_dim = 3 # x, y, prize
self.context_dim = embedding_dim + 1 # Embedding of last node + remaining prize to collect
self.init_embed = nn.Linear(node_dim, embedding_dim)
self.init_embed_depot = nn.Linear(2, embedding_dim) # depot embedding
def forward(self, input): # dict of 'loc', 'prize', 'depot'
# batch, 1, 2 -> batch, 1, embedding_dim
depot_embedding = self.init_embed_depot(input["depot"])[:, None, :]
# [batch, n_customer, 2, batch, n_customer, 1, batch, n_customer, 1] -> batch, n_customer, embedding_dim
node_embeddings = self.init_embed(
torch.cat((input["loc"], input["prize"][:, :, None]), -1)
)
# batch, n_customer+1, embedding_dim
out = torch.cat((depot_embedding, node_embeddings), 1)
return out
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