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"""
Problem specific node embedding for dynamic feature.
"""
import torch.nn as nn
def AutoDynamicEmbedding(problem_name, config):
"""
Automatically select the corresponding module according to ``problem_name``
"""
mapping = {
"tsp": NonDyanmicEmbedding,
"cvrp": NonDyanmicEmbedding,
"sdvrp": SDVRPDynamicEmbedding,
"pctsp": NonDyanmicEmbedding,
"op": NonDyanmicEmbedding,
}
embeddingClass = mapping[problem_name]
embedding = embeddingClass(**config)
return embedding
class SDVRPDynamicEmbedding(nn.Module):
"""
Embedding for dynamic node feature for the split delivery vehicle routing problem.
It is implemented as a linear projection of the demands left in each node.
Args:
embedding_dim: dimension of output
Inputs: state
* **state** : a class that provide ``state.demands_with_depot`` tensor
Outputs: glimpse_key_dynamic, glimpse_val_dynamic, logit_key_dynamic
* **glimpse_key_dynamic** : [batch, graph_size, embedding_dim]
* **glimpse_val_dynamic** : [batch, graph_size, embedding_dim]
* **logit_key_dynamic** : [batch, graph_size, embedding_dim]
"""
def __init__(self, embedding_dim):
super(SDVRPDynamicEmbedding, self).__init__()
self.projection = nn.Linear(1, 3 * embedding_dim, bias=False)
def forward(self, state):
glimpse_key_dynamic, glimpse_val_dynamic, logit_key_dynamic = self.projection(
state.demands_with_depot[:, 0, :, None].clone()
).chunk(3, dim=-1)
return glimpse_key_dynamic, glimpse_val_dynamic, logit_key_dynamic
class NonDyanmicEmbedding(nn.Module):
"""
Embedding for problems that do not have any dynamic node feature.
It is implemented as simply returning zeros.
Args:
embedding_dim: dimension of output
Inputs: state
* **state** : not used, just for consistency
Outputs: glimpse_key_dynamic, glimpse_val_dynamic, logit_key_dynamic
* **glimpse_key_dynamic** : [batch, graph_size, embedding_dim]
* **glimpse_val_dynamic** : [batch, graph_size, embedding_dim]
* **logit_key_dynamic** : [batch, graph_size, embedding_dim]
"""
def __init__(self, embedding_dim):
super(NonDyanmicEmbedding, self).__init__()
def forward(self, state):
return 0, 0, 0
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