| """ | |
| 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 | |