nn.Sequential fix

README.md

@@ -6,7 +6,7 @@
 ![GitHub repo size](https://img.shields.io/github/repo-size/QVPR/VPRTempo.svg?style=flat-square)
 [![PyPI downloads](https://img.shields.io/pypi/dw/VPRTempo.svg)](https://pypistats.org/packages/vprtempo)
 
-This repository contains code for VPRTempo, a spiking neural network that uses temporally encoding to perform visual place recognition tasks. The network is based off of [BLiTNet](https://arxiv.org/pdf/2208.01204.pdf) and adapted to the [VPRSNN](https://github.com/QVPR/VPRSNN) framework. 
+This repository contains code for [VPRTempo](vprtempo.github.io), a spiking neural network that uses temporally encoding to perform visual place recognition tasks. The network is based off of [BLiTNet](https://arxiv.org/pdf/2208.01204.pdf) and adapted to the [VPRSNN](https://github.com/QVPR/VPRSNN) framework. 
 
 <p style="width: 50%; display: block; margin-left: auto; margin-right: auto">
   <img src="./assets/github_image.png" alt="VPRTempo method diagram"/>
@@ -31,15 +31,14 @@ To use VPRTempo, please follow the instructions below for installation and usage
 ## License & Citation
 This repository is licensed under the [MIT License](./LICENSE) 
 
-If you use our code, please cite the following [paper](https://arxiv.org/abs/2309.10225):
+If you use our code, please cite our IEEE ICRA [paper](https://arxiv.org/abs/2309.10225):
 ```
-@misc{hines2023vprtempo,
+@inproceedings{hines2024vprtempo,
       title={VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition}, 
       author={Adam D. Hines and Peter G. Stratton and Michael Milford and Tobias Fischer},
-      year={2023},
-      eprint={2309.10225},
-      archivePrefix={arXiv},
-      primaryClass={cs.RO}
+      year={2024},
+      booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)}
+      
 }
 ```
 ## Installation and setup
@@ -106,9 +105,7 @@ For convenience, all data should be organised in the `./dataset` folder in the f
   |--winter
 ```
 ### Custom Datasets
-To define your own custom dataset to use with VPRTempo, you will need to follow the conventions for [PyTorch Datasets & Dataloaders](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html). We provide a simple script `./dataset/custom_dataset.py` which will rename images in user defined directories and generate the necessary `.csv` file to load into VPRTempo. 
-
-To learn how to use custom datasets, please see the [CustomDatasets.ipynb](https://github.com/AdamDHines/VPRTempo-quant/tree/main/tutorials) tutorial.
+To define your own custom dataset to use with VPRTempo, you will need to follow the conventions for [PyTorch Datasets & Dataloaders](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html). 
 
 ## Usage
 Running VPRTempo and VPRTempoQuant is handlded by `main.py`, which can be operated either through the command terminal or directly running the script. See below for more details.

main.py

@@ -226,7 +226,7 @@ def parse_network(use_quantize=False, train_new_model=False):
                             help="Ground truth tolerance for matching")
 
     # Define training parameters
-    parser.add_argument('--filter', type=int, default=1,
+    parser.add_argument('--filter', type=int, default=8,
                             help="Images to skip for training and/or inferencing")
     parser.add_argument('--epoch', type=int, default=4,
                             help="Number of epochs to train the model")

vprtempo/VPRTempo.py

@@ -131,11 +131,9 @@ def evaluate(self, models, test_loader):
         for model in models:
             self.inferences.append(nn.Sequential(
                 model.feature_layer.w,
-                nn.Hardtanh(0, 0.9),
-                nn.ReLU(),
+                nn.Hardtanh(0,1.0),
                 model.output_layer.w,
-                nn.Hardtanh(0, 0.9),
-                nn.ReLU()
+                nn.Hardtanh(0,1.0)
             ))
             self.inferences[-1].to(torch.device(self.device))
         # Initiliaze the output spikes variable

vprtempo/VPRTempoQuant.py

@@ -134,10 +134,8 @@ def evaluate(self, models, test_loader, layers=None):
             self.inferences.append(nn.Sequential(
                 model.feature_layer.w,
                 nn.Hardtanh(0, maxSpike),
-                nn.ReLU(),
                 model.output_layer.w,
-                nn.Hardtanh(0, maxSpike),
-                nn.ReLU()
+                nn.Hardtanh(0, maxSpike)
             ))
         # Initialize the tqdm progress bar
         pbar = tqdm(total=self.query_places,