This is the main model for Visual Place Recognition we use Pytorch Lightning for modularity purposes. Args: pl (_type_): _description_
| 33 | |
| 34 | |
| 35 | class VPRModel(nn.Module): |
| 36 | """This is the main model for Visual Place Recognition |
| 37 | we use Pytorch Lightning for modularity purposes. |
| 38 | |
| 39 | Args: |
| 40 | pl (_type_): _description_ |
| 41 | """ |
| 42 | |
| 43 | def __init__( |
| 44 | self, |
| 45 | # ---- Backbone |
| 46 | backbone_arch="resnet50", |
| 47 | backbone_config={}, |
| 48 | # ---- Aggregator |
| 49 | agg_arch="ConvAP", |
| 50 | agg_config={}, |
| 51 | ): |
| 52 | super().__init__() |
| 53 | |
| 54 | # Backbone |
| 55 | self.encoder_arch = backbone_arch |
| 56 | self.backbone_config = backbone_config |
| 57 | |
| 58 | # Aggregator |
| 59 | self.agg_arch = agg_arch |
| 60 | self.agg_config = agg_config |
| 61 | |
| 62 | # ---------------------------------- |
| 63 | # get the backbone and the aggregator |
| 64 | self.backbone = helper.get_backbone(backbone_arch, backbone_config) |
| 65 | self.aggregator = helper.get_aggregator(agg_arch, agg_config) |
| 66 | |
| 67 | # the forward pass of the lightning model |
| 68 | def forward(self, x): |
| 69 | x = self.backbone(x) |
| 70 | x = self.aggregator(x) |
| 71 | return x |
| 72 | |
| 73 | |
| 74 | class LoopDetector: |