diff --git a/.DS_Store b/.DS_Store
index da2f6b8..b9183bc 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..5aeef22
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,3 @@
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
new file mode 100644
index 0000000..0be658e
--- /dev/null
+++ b/.github/workflows/main.yml
@@ -0,0 +1,29 @@
+name: Publish to PyPi
+
+on:
+ release:
+ types: [created]
+
+jobs:
+ deploy:
+ runs-on: ubuntu-latest
+ steps:
+ - name: Check out code
+ uses: actions/checkout@v2
+
+ - name: Set up Python
+ uses: actions/setup-python@v2
+ with:
+ python-version: '3.x'
+
+ - name: Install dependencies
+ run: |
+ pip install setuptools wheel twine
+
+ - name: Build and publish
+ env:
+ TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
+ TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
+ run: |
+ python setup.py sdist bdist_wheel
+ twine upload dist/*
\ No newline at end of file
diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml
deleted file mode 100644
index 0a80fdb..0000000
--- a/.github/workflows/python-publish.yml
+++ /dev/null
@@ -1,29 +0,0 @@
-# This workflows will upload a Python Package using Twine when a release is created
-# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
-
-name: Upload Python Package
-
-on:
- release:
- types: [created]
-
-jobs:
- deploy:
- runs-on: ubuntu-latest
- steps:
- - uses: actions/checkout@v2
- - name: Set up Python
- uses: actions/setup-python@v2
- with:
- python-version: '3.x'
- - name: Install dependencies
- run: |
- python -m pip install --upgrade pip
- pip install setuptools wheel twine
- - name: Build and publish
- env:
- TWINE_USERNAME: __token__
- TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
- run: |
- python setup.py sdist bdist_wheel
- twine upload dist/*
diff --git a/.gitignore b/.gitignore
index 77305c4..f7b6871 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,17 @@
-weights/
-output/
__pycache__/
+.ipynb_checkpoints/
src/__pycache__/
+dataset/conv/
+dataset/Dusk/
+dataset/fall/
+dataset/model/
+dataset/output_database/
+dataset/output_query/
+dataset/Rain/
+dataset/spring/
+dataset/summer/
+dataset/Sun/
+dataset/winter/
+dataset/event.csv/
+models/VPRTempo78415685001.pth
+models/VPRTempoQuant78415685001.pth
diff --git a/README.md b/README.md
index 8a862ea..37ee3d8 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,10 @@
-# VPRTempo - Temporally encoded spiking neural network for visual place recognition
+# VPRTempo - A Temporally Encoded Spiking Neural Network for Visual Place Recognition
+
[](https://creativecommons.org/licenses/by-nc-sa/4.0/)
[](https://github.com/QVPR/VPRTempo/stargazers)
[](https://qcr.ai)
+[](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.
@@ -10,8 +12,24 @@ This repository contains code for VPRTempo, a spiking neural network that uses t
+VPRTempo is built on a [torch.nn](https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html) framework and employs custom learning rules based on the temporal codes of spikes in order to train layer weights.
+
+In this repository, we provide two networks:
+ - `VPRTempo`: Our base network architecture to perform visual place recognition (fp32)
+ - `VPRTempoQuant`: A modified base network with [Quantization Aware Training (QAT)](https://pytorch.org/docs/stable/quantization.html) enabled (int8)
+
+To use VPRTempo, please follow the instructions below for installation and usage.
+
+## :star: Update v1.1.0: What's new?
+ - Full integration of VPRTempo into torch.nn architecture
+ - Quantization Aware Training (QAT) enabled to train weights in int8 space
+ - Addition of tutorials in Jupyter Notebooks to learn how to use VPRTempo as well as explain the computational logic
+ - Simplification of weight operations, reducing to a single weight tensor - allowing positive and negative connections to change sign during training
+ - Easier dependency installation with PyPi/pip
+ - And more!
+
## License & Citation
-This repository is licensed under the [MIT License](./LICENSE)
+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):
```
@@ -25,35 +43,43 @@ If you use our code, please cite the following [paper](https://arxiv.org/abs/230
}
```
## Installation and setup
-We recommend installing dependencies for VPRTempo with [Mambaforge](https://mamba.readthedocs.io/en/latest/installation.html), however `conda` may also be used. VPRTempo uses [PyTorch](https://pytorch.org/) with the capability for [CUDA](https://developer.nvidia.com/cuda-toolkit) GPU acceleration. Follow the installation instructions based on your operating system and hardware specifications.
-
-### Windows & Linux
-#### CUDA enabled installation
-Use conda/mamba to create a new environment and install Python, CUDA tools, and dependencies.
-
+VPRTempo uses [PyTorch](https://pytorch.org/) with the capability for [CUDA](https://developer.nvidia.com/cuda-toolkit) acceleration. Please use one of the following options below to install the required dependencies, and if desired follow the instructions to install CUDA for your hardware and operating system.
+### Get the repository
+Download the Github repository.
```console
-conda create -n vprtempo -c pytorch -c nvidia python torchvision torchaudio pytorch-cuda=11.7 cudatoolkit opencv matplotlib
+git clone https://github.com/QVPR/VPRTempo.git
+cd ~/VPRTempo
```
-> **Note**
-> Install the version of PyTorch-CUDA that is compatible with your graphics card, see [Start Locally | PyTorch](https://pytorch.org/get-started/locally/) for more details.
+Once downloaded, please install the required dependencies to run the network through one of the following options:
-#### CPU only
-To install using the CPU only, simply install Python + dependencies.
-```console
-conda create -n vprtempo python pytorch torchvision torchaudio cpuonly opencv matplotlib -c pytorch
+### Option 1: Pip install
+Dependencies for VPRTempo can downloaded from our [PyPi package](https://pypi.org/project/VPRTempo/).
+
+```python
+pip3 install VPRTempo
```
-### MacOS
-CUDA acceleration is not available on MacOS and the network will only use the CPU, so simply just need to install Python + dependencies.
-```console
-conda create -n vprtempo -c conda-forge python opencv matplotlib -c pytorch pytorch::pytorch torchvision torchaudio
+If you wish to enable CUDA, please follow the instructions on the [PyTorch - Get Started](https://pytorch.org/get-started/locally/) page to install the required software versions for your hardware and operating system.
+
+### Option 2: Local requirements install
+Dependencies can be installed either through our provided `requirements.txt` files.
+
+```python
+pip3 install -r requirements.txt
```
+As above, if you wish to install CUDA please visit [PyTorch - Get Started](https://pytorch.org/get-started/locally/).
+### Option 3: Conda install
+>**:heavy_exclamation_mark: Recommended:**
+> Use [Mambaforge](https://mamba.readthedocs.io/en/latest/installation.html) instead of conda.
-### Get the repository
-Activate the environment & download the Github repository
```console
-conda activate vprtempo
-git clone https://github.com/QVPR/VPRTempo.git
-cd ~/VPRTempo
+# Windows/Linux - CUDA enabled
+conda create -n vprtempo -c pytorch -c nvidia python torchvision torchaudio pytorch-cuda=11.7 cudatoolkit prettytable tqdm numpy pandas scikit-learn
+
+# Windows/Linux - CPU only
+conda create -n vprtempo python pytorch torchvision torchaudio cpuonly prettytable tqdm numpy pandas scikit-learn -c pytorch
+
+# MacOS
+conda create -n vprtempo -c conda-forge python prettytable tqdm numpy pandas scikit-learn -c pytorch pytorch::pytorch torchvision torchaudio
```
## Datasets
@@ -64,37 +90,78 @@ VPRTempo was developed and tested using the [Nordland](https://webdiis.unizar.es
To simplify first usage, we have set the defaults in `VPRTempo.py` to train and test on a small subset of Nordland data. We recommend [downloading Nordland](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) and using the `./src/nordland.py` script to unzip and organize the images into the correct file and naming structure.
-### Custom datasets
-In general, data should be organised in the following way in order to train the network on multiple traversals of the same location.
+For convenience, all data should be organised in the `./dataset` folder in the following way in order to train the network on multiple traversals of the same location.
```
--dataset
- |--training
- | |--traversal_1
- | |--traversal_2
- |
- |--testing
- | |--test_traversal
+ |--summer
+ |--spring
+ |--fall
+ |--winter
```
-Speicfy the datapaths by altering `self.trainingPath` and `self.testPath` in `VPRTempo.py`. You can specify which traversals you want to train and test on by also altering `self.locations` and `self.test_location`. In the case above it would be the following;
+### 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.
-```python
-self.trainingPath = '/training/'
-self.testPath = '/testing/'
+To learn how to use custom datasets, please see the [CustomDatasets.ipynb](https://github.com/AdamDHines/VPRTempo-quant/tree/main/tutorials) tutorial.
-self.locations = ["traversal_1","traversal_2"]
-self.test_location = "test_traversal"
+## 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.
+### Prerequisites
+* Training and testing data is organized as above (see **Datasets** on how to set up the Nordland dataset)
+* The VPRTempo dependencies have been installed and/or the conda environment has been activated
+
+### Pretrained models
+We provide two pretrained models, for `VPRTempo` and `VPRTempoQuant`, that have learned a 500 place sequence from two Nordland traversals (Spring & Fall) which can be used to inference with Summer or Winter. To get the pretrained models, please download them:
+
+```console
+# Ensure your directory is set to VPRTempo
+cd ~/VPRTempo
+
+# If not already installed, install Git lfs
+git lfs install
+
+# Download the pretrained models
+git lfs pull
```
+### Run the inference network
+The `main.py` script handles running the inference network, there are two options:
-Image names for the same locations across traversals (training and testing) must be the same as they are imported based on a `.txt` file.
+#### Command terminal
+```console
+python main.py
+```
+
+ 
+
-## Usage
-Both the training and testing is handled by the `VPRTempo.py` script. Initial installs do not contain any pre-defined networks and will need to be trained prior to use.
-### Pre-requisites
-* Training and testing data is organized as above (see **Datasets** on how to set up the Nordland or custom datasets)
-* The VPRTempo `conda` environment has been activated
+To run the quantized network, parse the `--quantize` argument.
+```console
+python main.py --quantize
+```
+
+ 
+
+
+#### IDE
+You can also run VPRTempo through your IDE by running `main.py`. Change the `bool` flag for `use_quantize` to `True` if you wish to run VPRTempoQuant.
+
+### Train new network
+If you do not wish to use the pretrained models or you would like to train your own, we can parse the `--train_new_model` flag to `main.py`. Note, if a pretrained model already exists you will be prompted if you would like to retrain it.
+```console
+# For VPRTempo
+python main.py --train_new_model
+
+# For VPRTempoQuant
+python main.py --train_new_model --quantize
+```
+
+ 
+
+
+Similarly above, if you wish to run the training through an IDE then change the `bool` flag for `train_new_model` to `True`.
-Once these two things have been setup, run `VPRTempo.py` to train and test your first network with the default settings.
+## Tutorials
+We provide a series of Jupyter Notebook [tutorials](https://github.com/AdamDHines/VPRTempo-quant/tree/main/tutorials) that go through the basic operations and logic for VPRTempo and VPRTempoQuant.
## Issues, bugs, and feature requests
If you encounter problems whilst running the code or if you have a suggestion for a feature or improvement, please report it as an [issue](https://github.com/QVPR/VPRTempo/issues).
diff --git a/VPRTempo.egg-info/PKG-INFO b/VPRTempo.egg-info/PKG-INFO
new file mode 100644
index 0000000..2d362b3
--- /dev/null
+++ b/VPRTempo.egg-info/PKG-INFO
@@ -0,0 +1,126 @@
+Metadata-Version: 2.1
+Name: VPRTempo
+Version: 0.2.0
+Summary: VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition
+Home-page: https://github.com/QVPR/VPRTempo
+Author: Adam D Hines, Peter G Stratton, Michael Milford and Tobias Fischer
+Author-email: adam.hines@qut.edu.au
+License: MIT
+Keywords: python,place recognition,spiking neural networks,computer vision,robotics
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Requires-Python: >=3.6
+Description-Content-Type: text/markdown
+License-File: LICENSE
+
+# VPRTempo - A Temporally Encoded Spiking Neural Network for Visual Place Recognition
+
+[](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+[](https://github.com/QVPR/VPRTempo/stargazers)
+[](https://qcr.ai)
+
+[](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.
+
+
+
+
+
+VPRTempo is built on a [torch.nn](https://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html) network that employs custom learning rules based on the temporal codes of spikes in order to train layer weights.
+
+In this repository, we provide two networks:
+ - `VPRTempo`: Our base network architecture to perform visual place recognition (fp32)
+ - `VPRTempoQuant`: A modified base network with [Quantization Aware Training (QAT)](https://pytorch.org/docs/stable/quantization.html) enabled (int8)
+
+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):
+```
+@misc{hines2023vprtempo,
+ 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}
+}
+```
+## Installation and setup
+VPRTempo uses [PyTorch](https://pytorch.org/) with the capability for [CUDA](https://developer.nvidia.com/cuda-toolkit) acceleration. Please use one of the following options below to install the required dependencies, and if desired follow the instructions to install CUDA for your hardware and operating system.
+### Get the repository
+Download the Github repository.
+```console
+git clone https://github.com/QVPR/VPRTempo.git
+cd ~/VPRTempo
+```
+Once downloaded, please install the required dependencies to run the network through one of the following options:
+
+### Option 1: Pip install
+Dependencies for VPRTempo can downloaded from our [PyPi package](https://pypi.org/project/VPRTempo/).
+
+```python
+pip3 install vprtempo
+```
+If you wish to enable CUDA, please follow the instructions on the [PyTorch - Get Started](https://pytorch.org/get-started/locally/) page to install the required software versions for your hardware and operating system.
+
+### Option 2: Local requirements install
+Dependencies can be installed either through our provided `requirements.txt` files.
+
+```python
+pip3 install -r requirements.txt
+```
+As above, if you wish to install CUDA please visit [PyTorch - Get Started](https://pytorch.org/get-started/locally/).
+### Option 3: Conda install
+>**:heavy_exclamation_mark: Recommended:**
+> Use [Mambaforge](https://mamba.readthedocs.io/en/latest/installation.html) instead of conda.
+
+```console
+# Windows/Linux - CUDA enabled
+conda create -n vprtempo -c pytorch -c nvidia python torchvision torchaudio pytorch-cuda=11.7 cudatoolkit prettytable tqdm numpy pandas scikit-learn
+
+# Windows/Linux - CPU only
+conda create -n vprtempo python pytorch torchvision torchaudio cpuonly prettytable tqdm numpy pandas scikit-learn -c pytorch
+
+# MacOS
+conda create -n vprtempo -c conda-forge python prettytable tqdm numpy pandas scikit-learn -c pytorch pytorch::pytorch torchvision torchaudio
+```
+
+## Datasets
+VPRTempo was developed to be simple to train and test a variety of datasets. Please see the information below about running a test with the Nordland traversal dataset and how to organize custom datasets.
+
+### Nordland
+VPRTempo was developed and tested using the [Nordland](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) traversal dataset. This software will work for either the full-resolution or down-sampled datasets, however our paper details the full-resolution datasets.
+
+To simplify first usage, we have set the defaults in `VPRTempo.py` to train and test on a small subset of Nordland data. We recommend [downloading Nordland](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) and using the `./src/nordland.py` script to unzip and organize the images into the correct file and naming structure.
+
+### Custom datasets
+For convenience, all data should be organised in the `./dataset` folder in the following way in order to train the network on multiple traversals of the same location.
+
+```
+--dataset
+ |--traversal_1
+ |--traversal_2
+ |-- ...
+ |--test_traversal
+```
+Running `nordland.py` script will automatically do this for you.
+## 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.
+### Pre-requisites
+* Training and testing data is organized as above (see **Datasets** on how to set up the Nordland dataset)
+* The VPRTempo dependencies have been installed and/or the conda environment has been activated
+
+### Pre-trained model
+
+
+## Issues, bugs, and feature requests
+If you encounter problems whilst running the code or if you have a suggestion for a feature or improvement, please report it as an [issue](https://github.com/QVPR/VPRTempo/issues).
diff --git a/VPRTempo.egg-info/SOURCES.txt b/VPRTempo.egg-info/SOURCES.txt
new file mode 100644
index 0000000..5b53485
--- /dev/null
+++ b/VPRTempo.egg-info/SOURCES.txt
@@ -0,0 +1,9 @@
+LICENSE
+README.md
+main.py
+setup.py
+VPRTempo.egg-info/PKG-INFO
+VPRTempo.egg-info/SOURCES.txt
+VPRTempo.egg-info/dependency_links.txt
+VPRTempo.egg-info/requires.txt
+VPRTempo.egg-info/top_level.txt
\ No newline at end of file
diff --git a/VPRTempo.egg-info/dependency_links.txt b/VPRTempo.egg-info/dependency_links.txt
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/VPRTempo.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/VPRTempo.egg-info/requires.txt b/VPRTempo.egg-info/requires.txt
new file mode 100644
index 0000000..60165d6
--- /dev/null
+++ b/VPRTempo.egg-info/requires.txt
@@ -0,0 +1,8 @@
+torch
+torchvision
+torchaudio
+numpy
+pandas
+tqdm
+prettytable
+scikit-learn
diff --git a/VPRTempo.egg-info/top_level.txt b/VPRTempo.egg-info/top_level.txt
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/VPRTempo.egg-info/top_level.txt
@@ -0,0 +1 @@
+
diff --git a/VPRTempo.py b/VPRTempo.py
deleted file mode 100644
index f3896ad..0000000
--- a/VPRTempo.py
+++ /dev/null
@@ -1,620 +0,0 @@
-#MIT License
-
-#Copyright (c) 2023 Adam Hines, Peter G Stratton, Michael Milford, Tobias Fischer
-
-#Permission is hereby granted, free of charge, to any person obtaining a copy
-#of this software and associated documentation files (the "Software"), to deal
-#in the Software without restriction, including without limitation the rights
-#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-#copies of the Software, and to permit persons to whom the Software is
-#furnished to do so, subject to the following conditions:
-
-#The above copyright notice and this permission notice shall be included in all
-#copies or substantial portions of the Software.
-
-#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-#SOFTWARE.
-
-'''
-Imports
-'''
-
-import pickle
-import os
-import torch
-import gc
-import timeit
-
-import logging
-import sys
-sys.path.append('./src')
-sys.path.append('./weights')
-sys.path.append('./settings')
-sys.path.append('./output')
-
-import blitnet as bn
-import utils as ut
-import validation as val
-import numpy as np
-
-from os import path
-from datetime import datetime
-
-
-'''
-Spiking network model class
-'''
-class snn_model():
- def __init__(self):
- super().__init__()
-
- '''
- USER SETTINGS
- '''
- self.dataset = 'nordland' # set which dataset to run network on
- self.trainingPath = '' # training datapath
- self.testPath = '' # testing datapath
- self.number_modules = 1 # number of module networks
- self.number_training_images = 500 # Alter number of training images
- self.number_testing_images = 500 # Alter number of testing images
- self.locations = ["spring","fall"] # Define the datasets used in the training
- self.test_location = "summer" # Define the dataset is used for testing
- self.filter = 8 # Set to number of images to filter
- self.validation = True # Set to True to calculate PR metrics
-
- assert (len(self.dataset) != 0),"Dataset not defined, see README.md for details on setting up images"
- assert (os.path.isdir(self.trainingPath)),"Training path not set or path does not exist, specify for self.trainingPath"
- assert (os.path.isdir(self.testPath)),"Test path not set or path does not exist, specify for self.testPath"
- assert (os.path.isdir(self.trainingPath+self.locations[0])),"Images must be organized into folders based on locations, see README.md for details"
- assert (os.path.isdir(self.testPath+self.test_location)),"Images must be organized into folders based on locations, see README.md for details"
-
- '''
- NETWORK SETTINGS
- '''
- # Image and patch normalization settings
- self.imWidth = 28 # image width for resize
- self.imHeight = 28 # image height for resize
- self.num_patches = 7 # number of patches
- self.intensity = 255 # divide pixel values to get spikes in range [0,1]
- self.location_repeat = len(self.locations) # Number of training locations that are the same
-
- # Network and training settings
- self.input_layer = (self.imWidth*self.imHeight) # number of input layer neurons
- self.feature_layer = int(self.input_layer*2) # number of feature layer neurons
- self.output_layer = int(self.number_training_images/self.number_modules) # number of output layer neurons
- self.epoch = 4 # number of training iterations
- self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
- if self.device.type == "cuda": # clear cuda cache, initialize, and syncgronize gpu
- os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
- torch.cuda.set_device(self.device)
- gc.collect()
- torch.cuda.empty_cache()
- torch.cuda.init()
- torch.cuda.synchronize(device=self.device)
- self.T = int((self.number_training_images/self.number_modules)
- *self.location_repeat) # number of training steps
- self.annl_pow = 2 # learning rate anneal power
- self.imgs = {'training':[],'testing':[]}
- self.ids = {'training':[],'testing':[]}
- self.spike_rates = {'training':[],'testing':[]}
-
- # Hyperparamters
- self.theta_max = 0.5 # maximum threshold value
- self.n_init = 0.005 # initial learning rate value
- self.n_itp = 0.15 # initial intrinsic threshold plasticity rate[[0.9999]]
- self.f_rate = [0.2,0.9] # firing rate range
- self.p_exc = 0.1 # probability of excitatory connection
- self.p_inh = 0.5 # probability of inhibitory connection
- self.c= 0.1 # constant input
-
- # Test settings
- self.test_true = False # leave default to False
-
-
- '''
- DATA SETTINGS
- '''
- # Select training images from list
- with open('./'+self.dataset+'_imageNames.txt') as file:
- self.imageNames = [line.rstrip() for line in file]
-
- # Filter the loading images based on self.filter
- self.filteredNames = []
- for n in range(0,len(self.imageNames),self.filter):
- self.filteredNames.append(self.imageNames[n])
- del self.filteredNames[self.number_training_images:len(self.filteredNames)]
-
- # Get the full training and testing data paths
- self.fullTrainPaths = []
- for n in self.locations:
- self.fullTrainPaths.append(self.trainingPath+n+'/')
-
- # create output folder
- now = datetime.now()
- self.output_folder = './output/'+now.strftime("%d%m%y-%H-%M-%S")
- os.mkdir(self.output_folder)
-
- # setup logger
- self.logger = logging.getLogger("VPRTempo")
- self.logger.setLevel(logging.DEBUG)
- logging.basicConfig(filename=self.output_folder+"/logfile.log",
- filemode="a+",
- format="%(asctime)-15s %(levelname)-8s %(message)s")
- self.logger.addHandler(logging.StreamHandler())
-
- # Print network details
- self.logger.info('////////////')
- self.logger.info('VPRTempo - Temporally Encoded Visual Place Recognition v1.1.0-alpha')
- self.logger.info('Queensland University of Technology, Centre for Robotics')
- self.logger.info('')
- self.logger.info('© 2023 Adam D Hines, Peter G Stratton, Michael Milford, Tobias Fischer')
- self.logger.info('MIT license - https://github.com/QVPR/VPRTempo')
- self.logger.info('\\\\\\\\\\\\\\\\\\\\\\\\')
- self.logger.info('')
- self.logger.info('CUDA available: '+str(torch.cuda.is_available()))
- if torch.cuda.is_available() == True:
- current_device = torch.cuda.current_device()
- self.logger.info('Current device is: '+str(torch.cuda.get_device_name(current_device)))
- else:
- self.logger.info('Current device is: CPU')
- self.logger.info('')
- self.logger.info("~~ Hyperparameters ~~")
- self.logger.info('')
- self.logger.info('Firing threshold max: '+str(self.theta_max))
- self.logger.info('Initial STDP learning rate: '+str(self.n_init))
- self.logger.info('Intrinsic threshold plasticity learning rate: '+str(self.n_itp))
- self.logger.info('Firing rate range: ['+str(self.f_rate[0])+', '+str(self.f_rate[1])+']')
- self.logger.info('Excitatory connection probability: '+str(self.p_exc))
- self.logger.info('Inhibitory connection probability: '+str(self.p_inh))
- self.logger.info('Constant input: '+str(self.c))
- self.logger.info('')
- self.logger.info("~~ Training and testing conditions ~~")
- self.logger.info('')
- self.logger.info('Number of training images: '+str(self.number_training_images))
- self.logger.info('Number of testing images: '+str(self.number_testing_images))
- self.logger.info('Number of training epochs: '+str(self.epoch))
- self.logger.info('Number of modules: '+str(self.number_modules))
- self.logger.info('Dataset used: '+str(self.dataset))
- self.logger.info('Training locations: '+str(self.locations))
- self.logger.info('Testing location: '+str(self.test_location))
-
- # Network weights name
- self.training_out = './weights/'+str(self.input_layer)+'i'+\
- str(self.feature_layer)+\
- 'f'+str(self.output_layer)+\
- 'o'+str(self.epoch)+'/'
-
-
- # Check if pre-trained network exists, prompt if retrain or run
- def checkTrainTest(self):
- prompt = "A network with these parameters exists, re-train network? (y/n):\n"
- self.logger.info('')
- if path.isdir(self.training_out):
- retrain = input(prompt)
- else:
- retrain = 'y'
- return retrain
-
- def initialize(self,condition):
-
- '''
- Network startup and initialization
- '''
- self.logger.info('')
- self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
- self.logger.info(condition+' startup and initialization')
- self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
- self.logger.info('')
- self.logger.info('Loading '+condition+' images')
-
- if condition == 'testing':
- self.test_true = True
- del self.filteredNames[self.number_testing_images:len(self.filteredNames)]
- self.epoch = 1 # Only run the network once
- self.location_repeat = 1 # One location repeat for testing
- imgNum = self.number_testing_images
- else:
- imgNum = self.number_training_images
-
- # load the training images
- self.imgs[condition], self.ids[condition] = ut.loadImages(self.test_true,
- self.fullTrainPaths,
- self.filteredNames,
- [self.imWidth,self.imHeight],
- self.num_patches,
- self.testPath,
- self.test_location)
-
- self.spike_rates[condition] = ut.setSpikeRates(self.imgs[condition],
- self.ids[condition],
- self.device,
- [self.imWidth,self.imHeight],
- self.test_true,
- imgNum,
- self.number_modules,
- self.intensity,
- self.location_repeat)
-
- '''
- Run the training network
- '''
- def train(self):
-
- # remove contents of the weights folder
- ut.clear_weights(self.training_out)
-
- # create a new blitnet netowrk
- self.logger.info('Creating network and setting weights')
- net = bn.newNet(self.number_modules,self.imWidth*self.imHeight)
-
- # add the input layer
- bn.addLayer(net,[self.number_modules,1,self.input_layer],
- 0.0,0.0,0.0,0.0,0.0,False)
-
- # add the feature layer
- bn.addLayer(net,[self.number_modules,1,self.feature_layer],
- [0,self.theta_max],
- [self.f_rate[0],self.f_rate[1]],
- self.n_itp,
- [0,self.c],
- 0,
- False)
-
- # sequentially set the feature firing rates for the feature layer
- fstep = (self.f_rate[1]-self.f_rate[0])/self.feature_layer
-
- # loop through all modules and feature layer neurons
- for x in range(self.number_modules):
- for i in range(self.feature_layer):
- net['fire_rate'][1][x][:,i] = self.f_rate[0]+fstep*(i+1)
-
- # add excitatory inhibitory connections for input and feature layer
- bn.addWeights(net,0,1,[-1,0,1],[self.p_exc,self.p_inh],self.n_init)
- self.init_weights = [net['W'][0].clone().detach(),net['W'][1].clone().detach()]
-
- '''
- Feature layer training
- '''
- self.logger.info('')
- self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
- self.logger.info('Training the input to feature layer')
- self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
- self.logger.info('')
-
- self.logger.info('Setting spike rates from loaded images')
-
- # begin timer for network training
- start = timeit.default_timer()
-
- # Set the spikes times for the input images
- net['set_spks'][0] = torch.clone(self.spike_rates['training'])
- self.spike_rates['training'] = []
- if self.device == 'cuda':
- torch.cuda.empty_cache()
- gc.collect()
- layers = [len(net['W'])-2, len(net['W'])-1, len(net['W_lyr'])-1]
-
- # Train the input to feature layer for specified amount of epochs
- for epoch in range(self.epoch):
- net['step_num'] = 0
- epochStart = timeit.default_timer()
-
- # loop through each image and train the network
- for t in range(int(self.T)):
- bn.runSim(net,1,self.device,layers)
- # anneal learning rates
- if np.mod(t,10)==0:
- pt = pow(float(self.T-t)/self.T,self.annl_pow)
- net['eta_ip'][1] = self.n_itp*pt
- net['eta_stdp'][0] = self.n_init*pt
- net['eta_stdp'][1] = -1*self.n_init*pt
-
- # print training details
- self.logger.info('Epoch '+str(epoch+1)+' trained in: '
- +str(round(timeit.default_timer()-epochStart,2))+'s')
- self.logger.info('')
-
- self.logger.info('Finished training input to feature layer')
-
- '''
- Preparations for feature to output layer training
- '''
-
- # Turn off learning between input and feature layer
- net['eta_ip'][1] = 0.0
- if self.p_exc > 0.0: net['eta_stdp'][0] = 0.0
- if self.p_inh > 0.0: net['eta_stdp'][1] = 0.0
-
- self.logger.info('Getting feature layer spikes for output layer training')
-
- # get the feature spikes for training the output layer
- net['x_feat'] = []
- net['step_num'] = 0
- for t in range(int(self.T)): # run blitnet without learning to get feature spikes
- bn.runSim(net,1,self.device,layers)
- net['x_feat'].append(net['x'][1]) # dictionary output of feature spikes
-
- # delete input spikes
- net['set_spks'][0] = []
- if self.device == 'cuda':
- torch.cuda.empty_cache()
- gc.collect()
-
- self.logger.info('Creating output layer')
- # Create and train the output layer with the feature layer
- bn.addLayer(net,[self.number_modules,1,self.output_layer],
- 0.0,0.0,0.0,0.0,0.0,False)
-
- # Add excitatory and inhibitory connections
- bn.addWeights(net,1,2,[-1.0,0.0,1.0],[1.0,1.0],self.n_init)
- self.init_weights.append(net['W'][2].clone().detach())
- self.init_weights.append(net['W'][3].clone().detach())
-
- # Output spikes for spike forcing (final layer)
- out_spks = torch.tensor([0],device=self.device,dtype=float)
-
- '''
- Output layer training
- '''
- self.logger.info('')
- self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
- self.logger.info('Training the feature to output layer')
- self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
- self.logger.info('')
-
- net['spike_dims'] = 1 # change spike dims for output spike indexing
- net['set_spks'][0] = [] # remove input spikes
- layers = [len(net['W'])-2, len(net['W'])-1, len(net['W_lyr'])-1]
-
- # Train the feature to output layer for specified number of epochs
- for epoch in range(self.epoch):
- net['step_num'] = 0
- epochStart = timeit.default_timer()
-
- # Loop through all the spikes generated in the feature layer to train output
- for t in range(self.T):
- out_spks = torch.tensor([0],device=self.device,dtype=float)
-
- net['set_spks'][-1] = torch.tile(out_spks,
- (self.number_modules,
- 1,
- self.output_layer))
- net['x'][1] = net['x_feat'][t]
- bn.runSim(net,1,self.device,layers)
- # Anneal learning rates
- if np.mod(t,10)==0:
- pt = pow(float(self.T-t)/(self.T),self.annl_pow)
- net['eta_ip'][2] = self.n_itp*pt
- net['eta_stdp'][2] = self.n_init*pt
- net['eta_stdp'][3] = -1*self.n_init*pt
- if np.mod((t+1),(int(self.T/self.location_repeat))) == 0:
- net['step_num'] = 0
-
- # print training details
- self.logger.info('Epoch '+str(epoch+1)+' trained in: '
- +str(round(timeit.default_timer()-epochStart,2))+'s')
- self.logger.info('')
-
- self.logger.info('Finished training feature to output layer')
- self.logger.info('')
- self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~')
- self.logger.info('Network trained in '+str(round(timeit.default_timer()-start,2))
- +'s')
- self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~')
- self.logger.info('')
-
- # Turn off learning
- net['eta_ip'][2] = 0.0
- net['eta_stdp'][2] = 0.0
- net['eta_stdp'][3] = 0.0
-
- # Clear the network output spikes
- net['set_spks'][-1] = []
- net['spike_dims'] = self.input_layer
-
- # Reset network details
- net['sspk_idx'] = [0,0,0]
- net['step_num'] = 0
- net['spikes'] = [[],[],[]]
- net['x'] = [[],[],[]]
- net['x_feat'] = []
-
-
- self.logger.info('Network formatting and saving...')
-
- # Output the trained network
- outputPkl = self.training_out + 'net.pkl'
- with open(outputPkl, 'wb') as f:
- pickle.dump(net, f)
-
- # output the ground truth image names for later testing
- outputPkl = self.training_out + 'GT_imgnames.pkl'
- with open(outputPkl, 'wb') as f:
- pickle.dump(self.filteredNames, f)
-
- self.logger.info('Network succesfully saved!')
-
- # if using cuda, clear and dump the memory usage
- if self.device =='cuda':
- del net
- del self.spike_rates
- del self.imgs
- torch.cuda.empty_cache()
- gc.collect()
-
- '''
- Run the testing network
- '''
- def networktester(self):
-
- '''
- Network startup and initialization
- '''
-
- # unpickle the network
- self.logger.info('Unpickling the network')
- with open(self.training_out+'net.pkl', 'rb') as f:
- net = pickle.load(f)
-
- self.logger.info('Setting spike rates from loaded images')
- # calculate input spikes from training images
-
- net['set_spks'][0] = self.spike_rates['testing']
-
- # unpickle the ground truth image names
- with open(self.training_out+'GT_imgnames.pkl', 'rb') as f:
- GT_imgnames = pickle.load(f)
-
- self.logger.info('')
- self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
- self.logger.info('Running test network')
- self.logger.info('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
- self.logger.info('')
-
- # set number of correct places to 0
- numcorrect = 0
- net['spike_dims'] = self.input_layer
- numpconc = []
- start = timeit.default_timer()
- for t in range(self.number_testing_images):
- tonump = np.array([])
- bn.testSim(net,device=self.device)
- # output the index of highest amplitude spike
-
- tonump = np.append(tonump,np.reshape(net['x'][-1].cpu().numpy(),
- [1,1,int(self.number_training_images)]))
-
- # detect if no output
- if np.all(tonump == 0):
- # find the strongest sub-threshold input index
- nidx = np.argmax(torch.sub(net['x_input'][-1][0,0,:],
- net['thr'][-1][0,0,:]).cpu().numpy())
- tonump[nidx] = 0.5
- else:
- # find the highest output spike
- nidx = np.argmax(tonump)
-
- gt_ind = GT_imgnames.index(self.filteredNames[t])
-
- if gt_ind == nidx:
- numcorrect += 1
-
- if self.validation: # get similarity matrix for PR curve generation
- numpconc.append(tonump.tolist())
-
- self.p100r = round((numcorrect/self.number_testing_images)*100,2)
- self.logger.info('Number of correct matches P@100R - '+str(self.p100r)+'%')
-
- end = timeit.default_timer()
- queryHertz = self.number_testing_images/(end-start)
- self.logger.info('System queried at '+str(round(queryHertz,2))+'Hz')
-
- # if self.validation = True, get PR information and plot similarity matrix
- if self.validation:
- val.match_metrics(numpconc,
- self.output_folder,
- self.number_testing_images,
- self.number_training_images,
- self.logger)
-
- # if using cuda, clear and dump the memory usage
- if self.device =='cuda':
- gc.collect()
- torch.cuda.empty_cache()
-
- def sad(self):
-
- print('')
- print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
- print('Setting up Sum of Absolute Differences (SAD) calculations')
- print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
- print('')
-
- sadcorrect = 0
-
- # load the training images
- self.location_repeat = 1 # switch to only do SAD on one dataset traversal
- self.fullTrainPaths = self.fullTrainPaths[1]
- self.test_true = False # testing images preloaded, load the training ones
- # load the training images
- self.imgs['training'], self.ids['training'] = ut.loadImages(self.test_true,
- self.fullTrainPaths,
- self.filteredNames,
- [self.imWidth,self.imHeight],
- self.num_patches,
- self.testPath,
- self.test_location)
-
- # create database tensor
- for ndx, n in enumerate(self.imgs['training']):
- if ndx == 0:
- db = torch.unsqueeze(n,0)
- else:
- db = torch.concat((db,torch.unsqueeze(n,0)),0)
-
- def calc_sad(query, database, const):
-
- SAD = torch.sum(torch.abs(torch.sub((database * const), (query * const))),
- (1,2),keepdim=True)
- for n in range(2):
- SAD = torch.squeeze(SAD,-1)
- return SAD
-
- # calculate SAD for each image to database and count correct number
- imgred = 1/(self.imWidth*self.imHeight)
- sad_concat = []
- print('Running SAD')
-
- start = timeit.default_timer()
- for n, q in enumerate(self.imgs['testing']):
- pixels = torch.empty([])
- # create 3D tensor of query images
- for o in range(self.number_testing_images):
- if o == 0:
- pixels = torch.unsqueeze(q,0)
- else:
- pixels = torch.concat((pixels,torch.unsqueeze(q,0)),0)
-
- sad_score = calc_sad(pixels, db, imgred)
-
- best_match = np.argmin(sad_score.cpu().numpy())
- if n == best_match:
- sadcorrect += 1
-
- end = timeit.default_timer()
- p100r = round((sadcorrect/self.number_testing_images)*100,2)
- print('')
- print('Sum of absolute differences P@1: '+
- str(p100r)+'%')
- print('Sum of absolute differences queried at '
- +str(round(self.number_testing_images/(end-start),2))+'Hz')
-
-'''
-Run the network
-'''
-if __name__ == "__main__":
-
- # Instantiate model
- model = snn_model()
-
- # check if the network has already been trained previously
- flg = model.checkTrainTest()
-
- # if user inputs 'y' to retrain network if network doesn't exist
- if flg == 'y':
- model.initialize('training') # Initializes the training network
- model.train() # Run network training (will check if already trained)
- val.validate(model) # Validates that the network trained properly
-
- # Tests the network
- model.initialize('testing')
- model.networktester() # Test the network
- #model.sad()
- model.logger.info('')
- model.logger.info('VPRTempo run completed')
- model.logger.removeHandler(logging.StreamHandler()) # shut down the logger
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diff --git a/dataset/event.csv b/dataset/event.csv
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@@ -0,0 +1,160 @@
+Image_names,Index
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diff --git a/dataset/orc.csv b/dataset/orc.csv
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diff --git a/dist/VPRTempo-0.2.0.tar.gz b/dist/VPRTempo-0.2.0.tar.gz
new file mode 100644
index 0000000..ce1fe3e
Binary files /dev/null and b/dist/VPRTempo-0.2.0.tar.gz differ
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..a8f745a
--- /dev/null
+++ b/main.py
@@ -0,0 +1,163 @@
+#MIT License
+
+#Copyright (c) 2023 Adam Hines, Peter G Stratton, Michael Milford, Tobias Fischer
+
+#Permission is hereby granted, free of charge, to any person obtaining a copy
+#of this software and associated documentation files (the "Software"), to deal
+#in the Software without restriction, including without limitation the rights
+#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+#copies of the Software, and to permit persons to whom the Software is
+#furnished to do so, subject to the following conditions:
+
+#The above copyright notice and this permission notice shall be included in all
+#copies or substantial portions of the Software.
+
+#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+#SOFTWARE.
+
+'''
+Imports
+'''
+import argparse
+import sys
+sys.path.append('./src')
+sys.path.append('./networks/base')
+sys.path.append('./networks/quantized')
+
+import torch.quantization as quantization
+
+from VPRTempoTrain import VPRTempoTrain, generate_model_name, check_pretrained_model, train_new_model
+from VPRTempo import VPRTempo, run_inference
+from VPRTempoQuantTrain import VPRTempoQuantTrain, generate_model_name_quant, train_new_model_quant
+from VPRTempoQuant import VPRTempoQuant, run_inference_quant
+from loggers import model_logger, model_logger_quant
+
+def initialize_and_run_model(args,dims):
+ # If user wants to train a new network
+ if args.train_new_model:
+ # If using quantization aware training
+ if args.quantize:
+ models = []
+ logger = model_logger_quant()
+ # Get the quantization config
+ qconfig = quantization.get_default_qat_qconfig('fbgemm')
+ for _ in range(args.num_modules):
+ # Initialize the model
+ model = VPRTempoQuantTrain(args, dims, logger)
+ model.train()
+ model.qconfig = qconfig
+ models.append(model)
+ # Generate the model name
+ model_name = generate_model_name_quant(model)
+ # Check if the model has been trained before
+ check_pretrained_model(model_name)
+ # Train the model
+ train_new_model_quant(models, model_name, qconfig)
+ else: # Normal model
+ models = []
+ logger = model_logger()
+ for _ in range(args.num_modules):
+ # Initialize the model
+ model = VPRTempoTrain(args, dims, logger)
+ models.append(model)
+ # Generate the model name
+ model_name = generate_model_name(model)
+ # Check if the model has been trained before
+ check_pretrained_model(model_name)
+ # Train the model
+ train_new_model(models, model_name)
+ # Run the inference network
+ else:
+ # Set the quantization configuration
+ if args.quantize:
+ models = []
+ logger = model_logger_quant()
+ qconfig = quantization.get_default_qat_qconfig('fbgemm')
+ for _ in range(args.num_modules):
+ # Initialize the model
+ model = VPRTempoQuant(dims, args, logger)
+ model.eval()
+ model.qconfig = qconfig
+ model = quantization.prepare(model, inplace=False)
+ model = quantization.convert(model, inplace=False)
+ models.append(model)
+ # Generate the model name
+ model_name = generate_model_name_quant(model)
+ # Run the quantized inference model
+ run_inference_quant(models, model_name, qconfig)
+ else:
+ models = []
+ logger = model_logger()
+ for _ in range(args.num_modules):
+ # Initialize the model
+ model = VPRTempo(dims, args, logger)
+ models.append(model)
+ # Generate the model name
+ model_name = generate_model_name(model)
+ # Run the inference model
+ run_inference(models, model_name)
+
+def parse_network(use_quantize=False, train_new_model=False):
+ '''
+ Define the base parameter parser (configurable by the user)
+ '''
+ parser = argparse.ArgumentParser(description="Args for base configuration file")
+
+ # Define the dataset arguments
+ parser.add_argument('--dataset', type=str, default='nordland',
+ help="Dataset to use for training and/or inferencing")
+ parser.add_argument('--data_dir', type=str, default='./dataset/',
+ help="Directory where dataset files are stored")
+ parser.add_argument('--num_places', type=int, default=500,
+ help="Number of places to use for training and/or inferencing")
+ parser.add_argument('--num_modules', type=int, default=1,
+ help="Number of expert modules to use split images into")
+ parser.add_argument('--max_module', type=int, default=500,
+ help="Maximum number of images per module")
+ parser.add_argument('--database_dirs', nargs='+', default=['spring', 'fall'],
+ help="Directories to use for training")
+ parser.add_argument('--query_dir', nargs='+', default=['summer'],
+ help="Directories to use for testing")
+
+ # Define training parameters
+ 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")
+
+ # Define image transformation parameters
+ parser.add_argument('--patches', type=int, default=15,
+ help="Number of patches to generate for patch normalization image into")
+ parser.add_argument('--dims', type=str, default="56,56",
+ help="Dimensions to resize the image to")
+
+ # Define the network functionality
+ parser.add_argument('--train_new_model', action='store_true',
+ help="Flag to run the training or inferencing model")
+ parser.add_argument('--quantize', action='store_true',
+ help="Enable/disable quantization for the model")
+
+ # If the function is called with specific arguments, override sys.argv
+ if use_quantize or train_new_model:
+ sys.argv = ['']
+ if use_quantize:
+ sys.argv.append('--quantize')
+ if train_new_model:
+ sys.argv.append('--train_new_model')
+
+ # Output base configuration
+ args = parser.parse_args()
+ dims = [int(x) for x in args.dims.split(",")]
+
+ # Run the network with the desired settings
+ initialize_and_run_model(args,dims)
+
+if __name__ == "__main__":
+ # User input to determine if using quantized network or to train new model
+ parse_network(use_quantize=False,
+ train_new_model=False)
\ No newline at end of file
diff --git a/models/.DS_Store b/models/.DS_Store
new file mode 100644
index 0000000..28b3c56
Binary files /dev/null and b/models/.DS_Store differ
diff --git a/models/.gitkeep b/models/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/networks/base/VPRTempo.py b/networks/base/VPRTempo.py
new file mode 100644
index 0000000..18f4d17
--- /dev/null
+++ b/networks/base/VPRTempo.py
@@ -0,0 +1,230 @@
+#MIT License
+
+#Copyright (c) 2023 Adam Hines, Peter G Stratton, Michael Milford, Tobias Fischer
+
+#Permission is hereby granted, free of charge, to any person obtaining a copy
+#of this software and associated documentation files (the "Software"), to deal
+#in the Software without restriction, including without limitation the rights
+#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+#copies of the Software, and to permit persons to whom the Software is
+#furnished to do so, subject to the following conditions:
+
+#The above copyright notice and this permission notice shall be included in all
+#copies or substantial portions of the Software.
+
+#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+#SOFTWARE.
+
+'''
+Imports
+'''
+
+import os
+import torch
+import gc
+import sys
+sys.path.append('./src')
+sys.path.append('./models')
+sys.path.append('./output')
+sys.path.append('./dataset')
+
+import blitnet as bn
+import numpy as np
+import torch.nn as nn
+
+from dataset import CustomImageDataset, ProcessImage
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from prettytable import PrettyTable
+from metrics import recallAtK
+
+class VPRTempo(nn.Module):
+ def __init__(self, dims, args=None, logger=None):
+ super(VPRTempo, self).__init__()
+
+ # Set the arguments
+ if args is not None:
+ self.args = args
+ for arg in vars(args):
+ setattr(self, arg, getattr(args, arg))
+ setattr(self, 'dims', dims)
+ if torch.cuda.is_available():
+ self.device = "cuda:0"
+ else:
+ self.device = "cpu"
+
+ self.logger = logger
+ # Set the dataset file
+ self.dataset_file = os.path.join('./dataset', self.dataset + '.csv')
+
+ # Layer dict to keep track of layer names and their order
+ self.layer_dict = {}
+ self.layer_counter = 0
+
+ # Define layer architecture
+ self.input = int(self.dims[0]*self.dims[1])
+ self.feature = int(self.input * 2)
+ self.output = int(args.num_places / args.num_modules)
+
+ """
+ Define trainable layers here
+ """
+ self.add_layer(
+ 'feature_layer',
+ dims=[self.input, self.feature],
+ device=self.device,
+ inference=True
+ )
+ self.add_layer(
+ 'output_layer',
+ dims=[self.feature, self.output],
+ device=self.device,
+ inference=True
+ )
+
+ def add_layer(self, name, **kwargs):
+ """
+ Dynamically add a layer with given name and keyword arguments.
+
+ :param name: Name of the layer to be added
+ :type name: str
+ :param kwargs: Hyperparameters for the layer
+ """
+ # Check for layer name duplicates
+ if name in self.layer_dict:
+ raise ValueError(f"Layer with name {name} already exists.")
+
+ # Add a new SNNLayer with provided kwargs
+ setattr(self, name, bn.SNNLayer(**kwargs))
+
+ # Add layer name and index to the layer_dict
+ self.layer_dict[name] = self.layer_counter
+ self.layer_counter += 1
+
+ def evaluate(self, models, test_loader, layers=None):
+ """
+ Run the inferencing model and calculate the accuracy.
+
+ :param test_loader: Testing data loader
+ :param layers: Layers to pass data through
+ """
+ # Initialize the tqdm progress bar
+ pbar = tqdm(total=self.num_places,
+ desc="Running the test network",
+ position=0)
+ self.inferences = []
+ for model in models:
+ self.inferences.append(nn.Sequential(
+ model.feature_layer.w,
+ nn.Hardtanh(0, 0.9),
+ nn.ReLU(),
+ model.output_layer.w,
+ nn.Hardtanh(0, 0.9),
+ nn.ReLU()
+ ))
+ # Initiliaze the output spikes variable
+ out = []
+ # Run inference for the specified number of timesteps
+ for spikes, labels in test_loader:
+ # Set device
+ spikes, labels = spikes.to(self.device), labels.to(self.device)
+ # Forward pass
+ spikes = self.forward(spikes)
+ # Add output spikes to list
+ out.append(spikes.detach().cpu().tolist())
+ pbar.update(1)
+
+ # Close the tqdm progress bar
+ pbar.close()
+
+ # Rehsape output spikes into a similarity matrix
+ out = np.reshape(np.array(out),(model.num_places,model.num_places))
+
+ # Recall@N
+ N = [1,5,10,15,20,25] # N values to calculate
+ R = [] # Recall@N values
+ # Create GT matrix
+ GT = np.zeros((model.num_places,model.num_places), dtype=int)
+ for n in range(len(GT)):
+ GT[n,n] = 1
+ # Calculate Recall@N
+ for n in N:
+ R.append(round(recallAtK(out,GThard=GT,K=n),2))
+ # Print the results
+ table = PrettyTable()
+ table.field_names = ["N", "1", "5", "10", "15", "20", "25"]
+ table.add_row(["Recall", R[0], R[1], R[2], R[3], R[4], R[5]])
+ print(table)
+
+ def forward(self, spikes):
+ """
+ Compute the forward pass of the model.
+
+ Parameters:
+ - spikes (Tensor): Input spikes.
+
+ Returns:
+ - Tensor: Output after processing.
+ """
+
+ in_spikes = spikes.detach().clone()
+ outputs = [] # List to collect output tensors
+
+ for inference in self.inferences:
+ out_spikes = inference(in_spikes)
+ outputs.append(out_spikes) # Append the output tensor to the list
+
+ # Concatenate along the desired dimension
+ concatenated_output = torch.cat(outputs, dim=1)
+
+ return concatenated_output
+
+ def load_model(self, models, model_path):
+ """
+ Load pre-trained model and set the state dictionary keys.
+ """
+ combined_state_dict = torch.load(model_path, map_location=self.device)
+
+ for i, model in enumerate(models): # models_classes is a list of model classes
+ model.load_state_dict(combined_state_dict[f'model_{i}'])
+ model.eval() # Set the model to inference mode
+
+def run_inference(models, model_name):
+ """
+ Run inference on a pre-trained model.
+
+ :param model: Model to run inference on
+ :param model_name: Name of the model to load
+ :param qconfig: Quantization configuration
+ """
+ # Initialize the image transforms and datasets
+ image_transform = ProcessImage(models[0].dims, models[0].patches)
+ max_samples=models[0].num_places
+
+ test_dataset = CustomImageDataset(annotations_file=models[0].dataset_file,
+ base_dir=models[0].data_dir,
+ img_dirs=models[0].query_dir,
+ transform=image_transform,
+ skip=models[0].filter,
+ max_samples=max_samples)
+ # Initialize the data loader
+ test_loader = DataLoader(test_dataset,
+ batch_size=1,
+ shuffle=False,
+ num_workers=8,
+ persistent_workers=True)
+
+ # Load the model
+ models[0].load_model(models, os.path.join('./models', model_name))
+
+ # Retrieve layer names for inference
+ layer_names = list(models[0].layer_dict.keys())
+
+ # Use evaluate method for inference accuracy
+ with torch.no_grad():
+ models[0].evaluate(models, test_loader, layers=layer_names)
\ No newline at end of file
diff --git a/networks/base/VPRTempoTrain.py b/networks/base/VPRTempoTrain.py
new file mode 100644
index 0000000..077ccf3
--- /dev/null
+++ b/networks/base/VPRTempoTrain.py
@@ -0,0 +1,296 @@
+#MIT License
+
+#Copyright (c) 2023 Adam Hines, Peter G Stratton, Michael Milford, Tobias Fischer
+
+#Permission is hereby granted, free of charge, to any person obtaining a copy
+#of this software and associated documentation files (the "Software"), to deal
+#in the Software without restriction, including without limitation the rights
+#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+#copies of the Software, and to permit persons to whom the Software is
+#furnished to do so, subject to the following conditions:
+
+#The above copyright notice and this permission notice shall be included in all
+#copies or substantial portions of the Software.
+
+#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+#SOFTWARE.
+
+'''
+Imports
+'''
+
+import os
+import torch
+import gc
+import sys
+sys.path.append('./src')
+sys.path.append('./models')
+sys.path.append('./output')
+sys.path.append('./dataset')
+
+import blitnet as bn
+import numpy as np
+import torch.nn as nn
+import torchvision.transforms as transforms
+
+from loggers import model_logger
+from dataset import CustomImageDataset, ProcessImage
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+class VPRTempoTrain(nn.Module):
+ def __init__(self, args, dims, logger):
+ super(VPRTempoTrain, self).__init__()
+
+ # Set the arguments
+ self.args = args
+ for arg in vars(args):
+ setattr(self, arg, getattr(args, arg))
+ setattr(self, 'dims', dims)
+ if torch.cuda.is_available():
+ self.device = "cuda:0"
+ else:
+ self.device = "cpu"
+ self.logger = logger
+
+ # Set the dataset file
+ self.dataset_file = os.path.join('./dataset', self.dataset + '.csv')
+
+ # Layer dict to keep track of layer names and their order
+ self.layer_dict = {}
+ self.layer_counter = 0
+
+ # Define layer architecture
+ self.input = int(dims[0]*dims[1])
+ self.feature = int(self.input * 2)
+ self.output = int(args.num_places / args.num_modules)
+
+ # Set the total timestep count
+ self.location_repeat = len(args.database_dirs) # Number of times to repeat the locations
+ self.T = int((self.num_places/self.num_modules) * self.location_repeat * self.epoch)
+
+ """
+ Define trainable layers here
+ """
+ self.add_layer(
+ 'feature_layer',
+ dims=[self.input, self.feature],
+ thr_range=[0, 0.5],
+ fire_rate=[0.2, 0.9],
+ ip_rate=0.15,
+ stdp_rate=0.005,
+ p=[0.1, 0.5],
+ device=self.device
+ )
+ self.add_layer(
+ 'output_layer',
+ dims=[self.feature, self.output],
+ ip_rate=0.15,
+ stdp_rate=0.005,
+ p=[0.25, 0.75],
+ spk_force=True,
+ device=self.device
+ )
+
+ def add_layer(self, name, **kwargs):
+ """
+ Dynamically add a layer with given name and keyword arguments.
+
+ :param name: Name of the layer to be added
+ :type name: str
+ :param kwargs: Hyperparameters for the layer
+ """
+ # Check for layer name duplicates
+ if name in self.layer_dict:
+ raise ValueError(f"Layer with name {name} already exists.")
+
+ # Add a new SNNLayer with provided kwargs
+ setattr(self, name, bn.SNNLayer(**kwargs))
+
+ # Add layer name and index to the layer_dict
+ self.layer_dict[name] = self.layer_counter
+ self.layer_counter += 1
+
+ def model_logger(self):
+ """
+ Log the model configuration to the console.
+ """
+ model_logger(self)
+
+ def _anneal_learning_rate(self, layer, mod, itp, stdp):
+ """
+ Anneal the learning rate for the current layer.
+ """
+ if np.mod(mod, 100) == 0: # Modify learning rate every 100 timesteps
+ pt = pow(float(self.T - mod) / self.T, 2)
+ layer.eta_ip = torch.mul(itp, pt) # Anneal intrinsic threshold plasticity learning rate
+ layer.eta_stdp = torch.mul(stdp, pt) # Anneal STDP learning rate
+
+ return layer
+
+ def train_model(self, train_loader, layer, model, model_num, prev_layers=None):
+ """
+ Train a layer of the network model.
+
+ :param train_loader: Training data loader
+ :param layer: Layer to train
+ :param prev_layers: Previous layers to pass data through
+ """
+
+ # Initialize the tqdm progress bar
+ pbar = tqdm(total=int(self.T),
+ desc=f"Module {model_num+1}",
+ position=0)
+
+ # Initialize the learning rates for each layer (used for annealment)
+ init_itp = layer.eta_ip.detach()
+ init_stdp = layer.eta_stdp.detach()
+ mod = 0 # Used to determine the learning rate annealment, resets at each epoch
+
+ # idx scale factor for different modules
+ idx_scale = (self.max_module*self.filter)*model_num
+
+ # Run training for the specified number of epochs
+ for _ in range(self.epoch):
+ # Run training for the specified number of timesteps
+ for spikes, labels in train_loader:
+ spikes, labels = spikes.to(self.device), labels.to(self.device)
+ idx = torch.round((labels - idx_scale) / self.filter) # Set output index for spike forcing
+ # Pass through previous layers if they exist
+ if prev_layers:
+ with torch.no_grad():
+ for prev_layer_name in prev_layers:
+ prev_layer = getattr(model, prev_layer_name) # Get the previous layer object
+ spikes = self.forward(spikes, prev_layer) # Pass spikes through the previous layer
+ spikes = bn.clamp_spikes(spikes, prev_layer) # Clamp spikes [0, 0.9]
+ else:
+ prev_layer = None
+ # Get the output spikes from the current layer
+ pre_spike = spikes.detach() # Previous layer spikes for STDP
+ spikes = self.forward(spikes, layer) # Current layer spikes
+ spikes_noclp = spikes.detach() # Used for inhibitory homeostasis
+ spikes = bn.clamp_spikes(spikes, layer) # Clamp spikes [0, 0.9]
+ # Calculate STDP
+ layer = bn.calc_stdp(pre_spike,spikes,spikes_noclp,layer, idx, prev_layer=prev_layer)
+ # Adjust learning rates
+ layer = self._anneal_learning_rate(layer, mod, init_itp, init_stdp)
+ # Update the annealing mod & progress bar
+ mod += 1
+ pbar.update(1)
+
+ # Close the tqdm progress bar
+ pbar.close()
+
+ # Free up memory
+ if self.device == "cuda:0":
+ torch.cuda.empty_cache()
+ gc.collect()
+
+ def forward(self, spikes, layer):
+ """
+ Compute the forward pass of the model.
+
+ Parameters:
+ - spikes (Tensor): Input spikes.
+
+ Returns:
+ - Tensor: Output after processing.
+ """
+
+ spikes = layer.w(spikes)
+
+ return spikes
+
+ def save_model(self,models, model_out):
+ """
+ Save the trained model to models output folder.
+ """
+ state_dicts = {}
+ for i, model in enumerate(models): # Assuming models_list is your list of models
+ state_dicts[f'model_{i}'] = model.state_dict()
+
+ torch.save(state_dicts, model_out)
+
+def generate_model_name(model):
+ """
+ Generate the model name based on its parameters.
+ """
+ return ("VPRTempo" +
+ str(model.input) +
+ str(model.feature) +
+ str(model.output) +
+ str(model.num_modules) +
+ '.pth')
+
+def check_pretrained_model(model_name):
+ """
+ Check if a pre-trained model exists and prompt the user to retrain if desired.
+ """
+ if os.path.exists(os.path.join('./models', model_name)):
+ prompt = "A network with these parameters exists, re-train network? (y/n):\n"
+ retrain = input(prompt).strip().lower()
+ return retrain == 'n'
+ return False
+
+def train_new_model(models, model_name):
+ """
+ Train a new model.
+
+ :param model: Model to train
+ :param model_name: Name of the model to save after training
+ :param qconfig: Quantization configuration
+ """
+ # Initialize the image transforms and datasets
+ image_transform = transforms.Compose([
+ ProcessImage(models[0].dims, models[0].patches)
+ ])
+ # Automatically generate user_input_ranges
+ user_input_ranges = []
+ start_idx = 0
+
+ for _ in range(models[0].num_modules):
+ range_temp = [start_idx, start_idx+((models[0].max_module-1)*models[0].filter)]
+ user_input_ranges.append(range_temp)
+ start_idx = range_temp[1] + models[0].filter
+ if models[0].num_places < models[0].max_module:
+ max_samples=models[0].num_places
+ else:
+ max_samples = models[0].max_module
+ # Keep track of trained layers to pass data through them
+ trained_layers = []
+ # Training each layer
+ for layer_name, _ in sorted(models[0].layer_dict.items(), key=lambda item: item[1]):
+ print(f"Training layer: {layer_name}")
+ # Retrieve the layer object
+ for i, model in enumerate(models):
+ model.train()
+ layer = (getattr(model, layer_name))
+ img_range=user_input_ranges[i]
+ train_dataset = CustomImageDataset(annotations_file=models[0].dataset_file,
+ base_dir=models[0].data_dir,
+ img_dirs=models[0].database_dirs,
+ transform=image_transform,
+ skip=models[0].filter,
+ test=False,
+ img_range=img_range,
+ max_samples=max_samples)
+ # Initialize the data loader
+ train_loader = DataLoader(train_dataset,
+ batch_size=1,
+ shuffle=True,
+ num_workers=8,
+ persistent_workers=True)
+ # Train the layers
+ model.train_model(train_loader, layer, model, i, prev_layers=trained_layers)
+ # After training the current layer, add it to the list of trained layers
+ trained_layers.append(layer_name)
+ # Convert the model to evaluation mode
+ for model in models:
+ model.eval()
+ # Save the model
+ model.save_model(models,os.path.join('./models', model_name))
\ No newline at end of file
diff --git a/networks/quantized/VPRTempoQuant.py b/networks/quantized/VPRTempoQuant.py
new file mode 100644
index 0000000..f616289
--- /dev/null
+++ b/networks/quantized/VPRTempoQuant.py
@@ -0,0 +1,247 @@
+#MIT License
+
+#Copyright (c) 2023 Adam Hines, Peter G Stratton, Michael Milford, Tobias Fischer
+
+#Permission is hereby granted, free of charge, to any person obtaining a copy
+#of this software and associated documentation files (the "Software"), to deal
+#in the Software without restriction, including without limitation the rights
+#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+#copies of the Software, and to permit persons to whom the Software is
+#furnished to do so, subject to the following conditions:
+
+#The above copyright notice and this permission notice shall be included in all
+#copies or substantial portions of the Software.
+
+#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+#SOFTWARE.
+
+'''
+Imports
+'''
+
+import os
+import torch
+import subprocess
+import sys
+sys.path.append('./src')
+sys.path.append('./models')
+sys.path.append('./output')
+sys.path.append('./dataset')
+
+import blitnet as bn
+import numpy as np
+import torch.nn as nn
+import torch.quantization as quantization
+
+from loggers import model_logger_quant
+from VPRTempoQuantTrain import generate_model_name_quant
+from dataset import CustomImageDataset, ProcessImage
+from torch.utils.data import DataLoader
+from torch.ao.quantization import QuantStub, DeQuantStub
+from tqdm import tqdm
+from prettytable import PrettyTable
+from metrics import recallAtK
+
+#from main import parse_network
+
+class VPRTempoQuant(nn.Module):
+ def __init__(self, dims, args=None, logger=None):
+ super(VPRTempoQuant, self).__init__()
+
+ # Set the arguments
+ self.args = args
+ for arg in vars(args):
+ setattr(self, arg, getattr(args, arg))
+ setattr(self, 'dims', dims)
+ # Set the dataset file
+ self.dataset_file = os.path.join('./dataset', self.dataset + '.csv')
+
+ # Set the model logger and return the device
+ self.logger = logger
+ self.device = "cpu"
+
+ # Add quantization stubs for Quantization Aware Training (QAT)
+ self.quant = QuantStub()
+ self.dequant = DeQuantStub()
+
+ # Layer dict to keep track of layer names and their order
+ self.layer_dict = {}
+ self.layer_counter = 0
+
+ # Define layer architecture
+ self.input = int(dims[0]*dims[1])
+ self.feature = int(self.input * 2)
+ self.output = int(args.num_places / args.num_modules)
+
+ """
+ Define trainable layers here
+ """
+ self.add_layer(
+ 'feature_layer',
+ dims=[self.input, self.feature],
+ device=self.device,
+ inference=True
+ )
+ self.add_layer(
+ 'output_layer',
+ dims=[self.feature, self.output],
+ device=self.device,
+ inference=True
+ )
+
+ def add_layer(self, name, **kwargs):
+ """
+ Dynamically add a layer with given name and keyword arguments.
+
+ :param name: Name of the layer to be added
+ :type name: str
+ :param kwargs: Hyperparameters for the layer
+ """
+ # Check for layer name duplicates
+ if name in self.layer_dict:
+ raise ValueError(f"Layer with name {name} already exists.")
+
+ # Add a new SNNLayer with provided kwargs
+ setattr(self, name, bn.SNNLayer(**kwargs))
+
+ # Add layer name and index to the layer_dict
+ self.layer_dict[name] = self.layer_counter
+ self.layer_counter += 1
+
+ def evaluate(self, models, test_loader, layers=None):
+ """
+ Run the inferencing model and calculate the accuracy.
+
+ :param test_loader: Testing data loader
+ :param layers: Layers to pass data through
+ """
+ # Determine the Hardtahn max value
+ maxSpike = (1//models[0].quant.scale).item()
+ # Define the sequential inference model
+ self.inferences = []
+ for model in models:
+ 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()
+ ))
+ # Initialize the tqdm progress bar
+ pbar = tqdm(total=self.num_places,
+ desc="Running the test network",
+ position=0)
+ # Initiliaze the output spikes variable
+ out = []
+ # Run inference for the specified number of timesteps
+ for spikes, labels in test_loader:
+ # Set device
+ spikes, labels = spikes.to(self.device), labels.to(self.device)
+ # Pass through previous layers if they exist
+ spikes = self.forward(spikes)
+ # Add output spikes to list
+ out.append(spikes.detach().cpu().tolist())
+ pbar.update(1)
+
+ # Close the tqdm progress bar
+ pbar.close()
+
+ # Rehsape output spikes into a similarity matrix
+ out = np.reshape(np.array(out),(self.num_places,self.num_places))
+ # Calculate and print the Recall@N
+ N = [1,5,10,15,20,25]
+ R = []
+ # Create GT matrix
+ GT = np.zeros((self.num_places,self.num_places), dtype=int)
+ for n in range(len(GT)):
+ GT[n,n] = 1
+ for n in N:
+ R.append(recallAtK(out,GThard=GT,K=n))
+ # Print the results
+ table = PrettyTable()
+ table.field_names = ["N", "1", "5", "10", "15", "20", "25"]
+ table.add_row(["Recall", R[0], R[1], R[2], R[3], R[4], R[5]])
+ print(table)
+
+ def forward(self, spikes):
+ """
+ Compute the forward pass of the model.
+
+ Parameters:
+ - spikes (Tensor): Input spikes.
+
+ Returns:
+ - Tensor: Output after processing.
+ """
+ spikes = self.quant(spikes)
+ in_spikes = spikes.detach().clone()
+ outputs = [] # List to collect output tensors
+
+ for inference in self.inferences:
+ out_spikes = inference(in_spikes)
+ outputs.append(out_spikes) # Append the output tensor to the list
+
+ # Concatenate along the desired dimension
+ concatenated_output = torch.cat(outputs, dim=1)
+ spikes = self.dequant(concatenated_output)
+
+ return spikes
+
+ def load_model(self, models, model_path):
+ """
+ Load pre-trained model and set the state dictionary keys.
+ """
+ combined_state_dict = torch.load(model_path, map_location=self.device)
+
+ for i, model in enumerate(models): # models_classes is a list of model classes
+
+ model.load_state_dict(combined_state_dict[f'model_{i}'])
+ model.eval() # Set the model to inference mode
+
+def check_pretrained_model(model_name):
+ """
+ Check if a pre-trained model exists and tell user if it does not.
+ """
+ if not os.path.exists(os.path.join('./models', model_name)):
+ model.logger.info("A pre-trained network does not exist: please train one using VPRTempoQuant_Trainer")
+ pretrain = 'n'
+ else:
+ pretrain = 'y'
+ return pretrain
+
+def run_inference_quant(models, model_name, qconfig):
+ """
+ Run inference on a pre-trained model.
+
+ :param model: Model to run inference on
+ :param model_name: Name of the model to load
+ :param qconfig: Quantization configuration
+ """
+ max_samples = models[0].num_places
+ # Initialize the image transforms and datasets
+ image_transform = ProcessImage(models[0].dims, models[0].patches)
+ test_dataset = CustomImageDataset(annotations_file=models[0].dataset_file,
+ base_dir=models[0].data_dir,
+ img_dirs=models[0].query_dir,
+ transform=image_transform,
+ skip=models[0].filter,
+ max_samples=max_samples)
+ # Initialize the data loader
+ test_loader = DataLoader(test_dataset,
+ batch_size=1,
+ shuffle=False,
+ num_workers=8,
+ persistent_workers=True)
+
+ # Load the model
+ models[0].load_model(models, os.path.join('./models', model_name))
+
+ # Use evaluate method for inference accuracy
+ with torch.no_grad():
+ models[0].evaluate(models, test_loader)
\ No newline at end of file
diff --git a/networks/quantized/VPRTempoQuantTrain.py b/networks/quantized/VPRTempoQuantTrain.py
new file mode 100644
index 0000000..6c2448a
--- /dev/null
+++ b/networks/quantized/VPRTempoQuantTrain.py
@@ -0,0 +1,291 @@
+#MIT License
+
+#Copyright (c) 2023 Adam Hines, Peter G Stratton, Michael Milford, Tobias Fischer
+
+#Permission is hereby granted, free of charge, to any person obtaining a copy
+#of this software and associated documentation files (the "Software"), to deal
+#in the Software without restriction, including without limitation the rights
+#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+#copies of the Software, and to permit persons to whom the Software is
+#furnished to do so, subject to the following conditions:
+
+#The above copyright notice and this permission notice shall be included in all
+#copies or substantial portions of the Software.
+
+#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+#SOFTWARE.
+
+'''
+Imports
+'''
+
+import os
+import torch
+import gc
+import sys
+sys.path.append('./src')
+sys.path.append('./models')
+sys.path.append('./output')
+sys.path.append('./dataset')
+
+import blitnet as bn
+import numpy as np
+import torch.nn as nn
+import torch.quantization as quantization
+import torchvision.transforms as transforms
+
+from dataset import CustomImageDataset, ProcessImage
+from torch.utils.data import DataLoader
+from torch.ao.quantization import QuantStub, DeQuantStub
+from tqdm import tqdm
+
+class VPRTempoQuantTrain(nn.Module):
+ def __init__(self, args, dims, logger):
+ super(VPRTempoQuantTrain, self).__init__()
+
+ # Set the arguments
+ self.args = args
+ for arg in vars(args):
+ setattr(self, arg, getattr(args, arg))
+ setattr(self, 'dims', dims)
+
+ # Only CPU available for quantization
+ self.device = "cpu"
+
+ # Set the logger
+ self.logger = logger
+
+ # Set the dataset file
+ self.dataset_file = os.path.join('./dataset', self.dataset + '.csv')
+
+ # Add quantization stubs for Quantization Aware Training (QAT)
+ self.quant = QuantStub()
+ self.dequant = DeQuantStub()
+
+ # Layer dict to keep track of layer names and their order
+ self.layer_dict = {}
+ self.layer_counter = 0
+
+ # Define layer architecture
+ self.input = int(dims[0]*dims[1])
+ self.feature = int(self.input * 2)
+ self.output = int(args.num_places / args.num_modules)
+
+ # Set the total timestep count
+ self.location_repeat = len(args.database_dirs) # Number of times to repeat the locations
+ self.T = int((self.num_places / self.num_modules) * self.location_repeat * self.epoch)
+
+ """
+ Define trainable layers here
+ """
+ self.add_layer(
+ 'feature_layer',
+ dims=[self.input, self.feature],
+ thr_range=[0, 0.5],
+ fire_rate=[0.2, 0.9],
+ ip_rate=0.15,
+ stdp_rate=0.005,
+ p=[0.1, 0.5],
+ device=self.device
+ )
+ self.add_layer(
+ 'output_layer',
+ dims=[self.feature, self.output],
+ ip_rate=0.15,
+ stdp_rate=0.005,
+ spk_force=True,
+ p=[0.25, 0.75],
+ device=self.device
+ )
+
+ def add_layer(self, name, **kwargs):
+ """
+ Dynamically add a layer with given name and keyword arguments.
+
+ :param name: Name of the layer to be added
+ :type name: str
+ :param kwargs: Hyperparameters for the layer
+ """
+ # Check for layer name duplicates
+ if name in self.layer_dict:
+ raise ValueError(f"Layer with name {name} already exists.")
+
+ # Add a new SNNLayer with provided kwargs
+ setattr(self, name, bn.SNNLayer(**kwargs))
+
+ # Add layer name and index to the layer_dict
+ self.layer_dict[name] = self.layer_counter
+ self.layer_counter += 1
+
+ def _anneal_learning_rate(self, layer, mod, itp, stdp):
+ """
+ Anneal the learning rate for the current layer.
+ """
+ if np.mod(mod, 100) == 0: # Modify learning rate every 100 timesteps
+ pt = pow(float(self.T - mod) / self.T, 2)
+ layer.eta_ip = torch.mul(itp, pt) # Anneal intrinsic threshold plasticity learning rate
+ layer.eta_stdp = torch.mul(stdp, pt) # Anneal STDP learning rate
+
+ return layer
+
+ def train_model(self, train_loader, layer, model, model_num, prev_layers=None):
+ """
+ Train a layer of the network model.
+
+ :param train_loader: Training data loader
+ :param layer: Layer to train
+ :param prev_layers: Previous layers to pass data through
+ """
+
+ # Initialize the tqdm progress bar
+ pbar = tqdm(total=int(self.T),
+ desc="Training ",
+ position=0)
+
+ # Initialize the learning rates for each layer (used for annealment)
+ init_itp = layer.eta_ip.detach()
+ init_stdp = layer.eta_stdp.detach()
+ mod = 0 # Used to determine the learning rate annealment, resets at each epoch
+ # idx scale factor for different modules
+ idx_scale = (self.max_module*self.filter)*model_num
+ # Run training for the specified number of epochs
+ for epoch in range(self.epoch):
+ # Run training for the specified number of timesteps
+ for spikes, labels in train_loader:
+ spikes, labels = spikes.to(self.device), labels.to(self.device)
+ idx = torch.round((labels - idx_scale) / self.filter) # Set output index for spike forcing
+ # Pass through previous layers if they exist
+ if prev_layers:
+ with torch.no_grad():
+ for prev_layer_name in prev_layers:
+ prev_layer = getattr(self, prev_layer_name) # Get the previous layer object
+ spikes = self.forward(spikes, prev_layer) # Pass spikes through the previous layer
+ spikes = bn.clamp_spikes(spikes, prev_layer) # Clamp spikes [0, 0.9]
+ else:
+ prev_layer = None
+ # Get the output spikes from the current layer
+ pre_spike = spikes.detach() # Previous layer spikes for STDP
+ spikes = self.forward(spikes, layer) # Current layer spikes
+ spikes_noclp = spikes.detach() # Used for inhibitory homeostasis
+ spikes = bn.clamp_spikes(spikes, layer) # Clamp spikes [0, 0.9]
+ # Calculate STDP
+ layer = bn.calc_stdp(pre_spike,spikes,spikes_noclp,layer, idx, prev_layer=prev_layer)
+ # Adjust learning rates
+ layer = self._anneal_learning_rate(layer, mod, init_itp, init_stdp)
+ # Update the annealing mod & progress bar
+ mod += 1
+ pbar.update(1)
+
+ # Close the tqdm progress bar
+ pbar.close()
+
+ def forward(self, spikes, layer):
+ """
+ Compute the forward pass of the model.
+
+ Parameters:
+ - spikes (Tensor): Input spikes.
+
+ Returns:
+ - Tensor: Output after processing.
+ """
+
+ spikes = self.quant(spikes)
+ spikes = layer.w(spikes)
+ spikes = self.dequant(spikes)
+
+ return spikes
+
+ def save_model(self, models, model_out):
+ """
+ Save the trained model to models output folder.
+ """
+ state_dicts = {}
+ for i, model in enumerate(models): # Assuming models_list is your list of models
+ state_dicts[f'model_{i}'] = model.state_dict()
+
+ torch.save(state_dicts, model_out)
+
+def generate_model_name_quant(model):
+ """
+ Generate the model name based on its parameters.
+ """
+ return ("VPRTempoQuant" +
+ str(model.input) +
+ str(model.feature) +
+ str(model.output) +
+ str(model.num_modules) +
+ '.pth')
+
+def check_pretrained_model(model_name):
+ """
+ Check if a pre-trained model exists and prompt the user to retrain if desired.
+ """
+ if os.path.exists(os.path.join('./models', model_name)):
+ prompt = "A network with these parameters exists, re-train network? (y/n):\n"
+ retrain = input(prompt).strip().lower()
+ return retrain == 'n'
+ return False
+
+def train_new_model_quant(models, model_name, qconfig):
+ """
+ Train a new model.
+
+ :param model: Model to train
+ :param model_name: Name of the model to save after training
+ :param qconfig: Quantization configuration
+ """
+ # Initialize the image transforms and datasets
+ image_transform = transforms.Compose([
+ ProcessImage(models[0].dims, models[0].patches)
+ ])
+ # Automatically generate user_input_ranges
+ user_input_ranges = []
+ start_idx = 0
+
+ for _ in range(models[0].num_modules):
+ range_temp = [start_idx, start_idx+((models[0].max_module-1)*models[0].filter)]
+ user_input_ranges.append(range_temp)
+ start_idx = range_temp[1] + models[0].filter
+ if models[0].num_places < models[0].max_module:
+ max_samples=models[0].num_places
+ else:
+ max_samples = models[0].max_module
+ # Keep track of trained layers to pass data through them
+ # Training each layer
+ trained_models = []
+ for i, model in enumerate(models):
+ trained_layers = []
+ img_range=user_input_ranges[i]
+ train_dataset = CustomImageDataset(annotations_file=models[0].dataset_file,
+ base_dir=models[0].data_dir,
+ img_dirs=models[0].database_dirs,
+ transform=image_transform,
+ skip=models[0].filter,
+ test=False,
+ img_range=img_range,
+ max_samples=max_samples)
+ # Initialize the data loader
+ train_loader = DataLoader(train_dataset,
+ batch_size=1,
+ shuffle=True,
+ num_workers=8,
+ persistent_workers=True)
+ model = quantization.prepare_qat(model, inplace=False)
+ for layer_name, _ in sorted(models[0].layer_dict.items(), key=lambda item: item[1]):
+ print(f"Training layer: {layer_name}")
+ layer = (getattr(model, layer_name))
+
+ # Train the layers
+ model.train_model(train_loader, layer, model, i, prev_layers=trained_layers)
+ trained_layers.append(layer_name)
+ trained_models.append(quantization.convert(model, inplace=False))
+ # After training the current layer, add it to the list of trained layer
+
+ # Save the model
+ model.save_model(trained_models,os.path.join('./models', model_name))
\ No newline at end of file
diff --git a/nordland_imageNames.txt b/nordland_imageNames.txt
deleted file mode 100644
index 01543f2..0000000
--- a/nordland_imageNames.txt
+++ /dev/null
@@ -1,27592 +0,0 @@
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diff --git a/orc_imageNames.txt b/orc_imageNames.txt
deleted file mode 100644
index 588946c..0000000
--- a/orc_imageNames.txt
+++ /dev/null
@@ -1,3369 +0,0 @@
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diff --git a/output/.gitkeep b/output/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/output/.output.txt b/output/.output.txt
deleted file mode 100644
index 6d575ae..0000000
--- a/output/.output.txt
+++ /dev/null
@@ -1 +0,0 @@
-# Hidden file for output folder
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..60165d6
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,8 @@
+torch
+torchvision
+torchaudio
+numpy
+pandas
+tqdm
+prettytable
+scikit-learn
diff --git a/setup.py b/setup.py
index ecb2740..1252a7d 100644
--- a/setup.py
+++ b/setup.py
@@ -9,22 +9,20 @@
# define the base requires needed for the repo
requirements = [
- 'matplotlib',
'torch',
'torchvision',
'torchaudio',
+ 'numpy',
+ 'pandas',
+ 'tqdm',
+ 'prettytable',
+ 'scikit-learn'
]
-# workaround as opencv-python does not show up in "pip list" within a conda environment
-# we do not care as conda recipe has py-opencv requirement anyhow
-is_conda = os.path.exists(os.path.join(sys.prefix, 'conda-meta'))
-if not is_conda:
- requirements.append('opencv-python')
-
# define the setup
setup(
name="VPRTempo",
- version="1.0.1",
+ version="1.1.0",
description='VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition',
long_description=long_description,
long_description_content_type='text/markdown',
@@ -33,12 +31,12 @@
url='https://github.com/QVPR/VPRTempo',
license='MIT',
install_requires=requirements,
- python_requires='>=3.8',
+ python_requires='>=3.6',
classifiers=[
# 3 - Alpha
# 4 - Beta
# 5 - Production/Stable
- 'Development Status :: 4 - Beta',
+ 'Development Status :: 5 - Production/Stable',
# Indicate who your project is intended for
'Intended Audience :: Developers',
@@ -55,6 +53,5 @@
packages=find_packages(),
keywords=['python', 'place recognition', 'spiking neural networks',
'computer vision', 'robotics'],
- scripts=['VPRTempo.py'],
- package_data={'':['nordland_imageNames.txt','orc_imageNames.txt']}
+ scripts=['main.py'],
)
diff --git a/src/.DS_Store b/src/.DS_Store
index d121930..1e5c5df 100644
Binary files a/src/.DS_Store and b/src/.DS_Store differ
diff --git a/src/blitnet.py b/src/blitnet.py
index a0d84fb..ea97809 100644
--- a/src/blitnet.py
+++ b/src/blitnet.py
@@ -23,423 +23,221 @@
'''
Imports
'''
-import numpy as np
-import pdb
import torch
-import matplotlib.pyplot as plt
-
-
-##################################
-# Return a new empty BITnet instance
-
-def newNet(modules, dims):
-
- np.random.seed() # new random seed
-
- # ** NEURON FIELDS **
- # x = activations
- # x_input = total inputs
- # x_prev = previous activations
- # x_calc = calculated activations
- # x_fastinp = total inputs including fast inhib
- # dim = dimensions
- # thr = thresholds for each neuron
- # fire_rate = target firing rate for each neuron
- # have_rate = have a target firing rate
- # mean_rate = running avg firing rate for each neuron
- # eta_ip = IP (threshold) learning rate
- # const_inp = constant input to each neuron
- # nois = noise st.dev.
- # set_spks = pre-defined spike times (if any)
- # sspk_idx = current index into set_spks
- # spikes = spike events
- # rec_spks = record spikes?
- #
- # ** CONNECTION FIELDS **
- # W = weights (-ve for inhib synapses)
- # I = synaptic currents
- # is_inhib = inhib weights flag
- # W_lyr = pre and post layer numbers
- # eta_stdp = STDP learning rate (-ve for inhib synapses)
- #
- # ** SIMULATION FIELDS **
- # step_num = current step
-
- #pdb.set_trace()
- net = dict(x=[],x_input=[],x_prev=[],x_calc=[],x_fastinp=[],dim=[],thr=[],
- fire_rate=[],have_rate=[],mean_rate=[],eta_ip=[],const_inp=[],nois=[],
- set_spks=[],sspk_idx=[],spikes=[],rec_spks=[],
- W=[],I=[],is_inhib=[],W_lyr=[],eta_stdp=[],
- step_num=0, num_modules = modules, spike_dims = dims)
-
- return net
+import torch.nn as nn
+import numpy as np
-##################################
-# Add a neuron layer (ie a neuron population)
-# net: BITnet instance
-# dim: layer dimensions [x,y,...]
-# thr_range: initial threshold range
-# fire_rate: target firing rate (0=no target)
-# ip_rate: intrinsic threshold plasticity (IP) rate (0=no IP)
-# const_inp: constant input to each neuron (0=none)
-# nois: noise variance (0=no noise)
-# rec_spks: record spikes?
-def addLayer(net,dims,thr_range,fire_rate,ip_rate,const_inp,nois,rec_spks):
-
- # get torch device
- device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
- # Check constraints etc
- if np.isscalar(thr_range): thr_range = [thr_range,thr_range]
- if np.isscalar(fire_rate): fire_rate = [fire_rate,fire_rate]
- if np.isscalar(const_inp): const_inp = [const_inp,const_inp]
-
- # create layer tensors
- net['dim'].append(np.array(dims,int))
- net['x'].append(torch.zeros(dims[0],dims[1],dims[2],device=device))
- net['x_prev'].append(torch.zeros(dims[0],dims[1],dims[2],device=device))
- net['x_calc'].append(torch.zeros(dims[0],dims[1],dims[2],device=device))
- net['x_input'].append(torch.zeros(dims[0],dims[1],dims[2],device=device))
- net['x_fastinp'].append(torch.zeros(dims[0],dims[1],dims[2],device=device))
- net['eta_ip'].append(ip_rate)
- temp_thr = torch.zeros(dims[0],dims[1],dims[2], device=device)
- net['thr'].append(temp_thr.uniform_(thr_range[0],thr_range[1]))
- temp_fire = torch.zeros(dims[0],dims[1],dims[2], device=device)
- net['fire_rate'].append(temp_fire.uniform_(fire_rate[0],fire_rate[1]))
- net['have_rate'].append(any(net['fire_rate'][-1][:,:,0]>0.0))
-
- temp_const = torch.zeros(dims[0],dims[1],dims[2], device=device)
- net['const_inp'].append(temp_const.uniform_(const_inp[0],const_inp[1]))
+class SNNLayer(nn.Module):
+ def __init__(self, dims=[0,0],thr_range=[0,0],fire_rate=[0,0],ip_rate=0,
+ stdp_rate=0,const_inp=[0,0],p=[1,1],spk_force=False,device=None,inference=False,args=None):
+ super(SNNLayer, self).__init__()
+ """
+ dims: [input, output] dimensions of the layer
+ thr_range: [min, max] range of thresholds
+ fire_rate: [min, max] range of firing rates
+ ip_rate: learning rate for input threshold plasticity
+ stdp_rate: learning rate for stdp
+ const_inp: [min, max] range of constant input
+ p: [min, max] range of connection probabilities
+ spk_force: boolean to force spikes
+ """
+
+ # Device
+ self.device = device
+ # Add different parameters depending if trainnig or running inference model
+ if inference: # If running inference model
+ self.w = nn.Linear(dims[0], dims[1], bias=False) # Combined weight tensors
+ self.w.to(device)
+ self.thr = nn.Parameter(torch.zeros([1, dims[-1]],
+ device=self.device).uniform_(thr_range[0],
+ thr_range[1]))
+ else: # If training new model
+ # Check constraints etc
+ if np.isscalar(thr_range): thr_range = [thr_range, thr_range]
+ if np.isscalar(fire_rate): fire_rate = [fire_rate, fire_rate]
+ if np.isscalar(const_inp): const_inp = [const_inp, const_inp]
+
+ # Initialize Tensors
+ self.x = torch.zeros([1, dims[-1]], device=self.device)
+ self.eta_ip = torch.tensor(ip_rate, device=self.device)
+ self.eta_stdp = torch.tensor(stdp_rate, device=self.device)
+
+ # Initialize Parameters
+ self.thr = nn.Parameter(torch.zeros([1, dims[-1]],
+ device=self.device).uniform_(thr_range[0],
+ thr_range[1]))
+ self.fire_rate = torch.zeros([1,dims[-1]], device=self.device).uniform_(fire_rate[0], fire_rate[1])
+
+ # Sequentially set the feature firing rates (if any)
+ if not torch.all(self.fire_rate==0).item():
+ fstep = (fire_rate[1]-fire_rate[0])/dims[-1]
+
+ for i in range(dims[-1]):
+ self.fire_rate[:,i] = fire_rate[0]+fstep*(i+1)
+
+ self.have_rate = torch.any(self.fire_rate[:,0] > 0.0).to(self.device)
+ self.const_inp = torch.zeros([1, dims[-1]], device=self.device).uniform_(const_inp[0], const_inp[1])
+ self.p = p
+ self.dims = dims
+
+ # Additional State Variables
+ self.set_spks = []
+ self.sspk_idx = 0
+ self.spikes = torch.empty([], dtype=torch.float64)
+ self.spk_force = spk_force
+
+ # Create the excitatory weights
+ self.exc = nn.Linear(dims[0], dims[1], bias=False)
+ self.exc.weight = self.addWeights(dims=dims,
+ W_range=[0,1],
+ p=p[0],
+ device=device)
+
+ # Create the inhibitory weights
+ self.inh = nn.Linear(dims[0], dims[1], bias=False)
+ self.inh.weight = self.addWeights(dims=dims,
+ W_range=[-1,0],
+ p=p[-1],
+ device=device)
+
+ # Output boolean reference of which neurons have connection weights
+ self.havconnExc = self.exc.weight > 0
+ self.havconnInh = self.inh.weight < 0
- net['nois'].append(nois)
- net['set_spks'].append([])
- net['sspk_idx'].append(0)
- net['spikes'].append(torch.empty([],dtype=torch.float64))
- net['rec_spks'].append(rec_spks)
+ # Combine weights into a single tensor
+ self.w = nn.Linear(dims[0], dims[1], bias=False)
+ self.w.weight = nn.Parameter(torch.add(self.exc.weight, self.inh.weight))
-##################################
-# Add a set of random connections between layers
-# net: BITnet instance
-# layer_pre: presynaptic layer
-# layer_post: postsynaptic layer
-# W_range: weight range [lo,hi]
-# p: initial connection probability
-# stdp_rate: STDP rate (0=no STDP)
+ self.havconnCombinedExc = self.w.weight > 0
+ self.havconnCombinedInh = self.w.weight < 0
-def addWeights(net,layer_pre,layer_post,W_range,p,stdp_rate):
+ del self.exc, self.inh
- # get torch device
- device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+ def addWeights(self,W_range=[0,0],p=[0,0],dims=[0,0],device=None):
- # Check constraints etc
- if np.isscalar(W_range): W_range = [W_range,W_range]
-
- # determine dimensions of the weight matrices
- nrow =net['x'][layer_pre].size(dim=2)
- ncol = net['x'][layer_post].size(dim=2)
+ # Get torch device
+ device = device
- # calculate mean and std for normal distributions
- inWmn = (W_range[0]+W_range[1])/2.0
- inWsd = (W_range[1]-W_range[0])/6.0
- exWmn = (W_range[1]+W_range[2])/2.0
- exWsd = (W_range[2]-W_range[1])/6.0
-
- # loop through modules and add excitatory and inhibitory weights
- for n in range(net['num_modules']):
- if n == 0: # first weights to be appended
- net['W'].append(torch.empty(nrow,ncol,device=device).normal_(mean=exWmn,std=exWsd)) # excitatory weights
- excIndex = len(net['W']) - 1
- net['W'][excIndex] = torch.unsqueeze(net['W'][excIndex],0)
- net['W'].append(torch.empty(nrow,ncol,device=device).normal_(mean=inWmn,std=inWsd)) # inhibitory weights
- inhIndex = len(net['W']) - 1
- net['W'][inhIndex] = torch.unsqueeze(net['W'][inhIndex],0)
- else: # stack new weights onto appended weight
- net['W'][excIndex] = torch.concat((net['W'][excIndex],
- torch.unsqueeze(torch.empty(nrow,ncol,device=device).normal_(mean=exWmn,std=exWsd),0)),0)
- net['W'][inhIndex] = torch.concat((net['W'][inhIndex],
- torch.unsqueeze(torch.empty(nrow,ncol,device=device).normal_(mean=inWmn,std=inWsd),0)),0) # inhibitory weights
- net['W'][excIndex][n][net['W'][excIndex][n] < 0] = 0.0 # remove -ve excitatory weights
- net['W'][inhIndex][n][net['W'][inhIndex][n] > 0] = 0.0 # remove +ve inhibitory weights
+ # Check constraints etc
+ if np.isscalar(W_range): W_range = [W_range,W_range]
- # remove connections based on exc and inh probabilities
- setzeroExc = np.random.rand(nrow,ncol) > p[0]
- setzeroInh = np.random.rand(nrow,ncol) > p[1]
+ # Determine dimensions of the weight matrices
+ nrow = dims[1]
+ ncol = dims[0]
- # add current
- if n == 0:
- Iindex = len(net['I'])
- net['I'].append(torch.zeros(nrow, device=device))
- net['I'][Iindex] = torch.unsqueeze(net['I'][Iindex],0)
- # append single reference arguments
- net['W_lyr'].append([layer_pre,layer_post])
- net['eta_stdp'].append(stdp_rate)
- net['eta_stdp'].append(-stdp_rate)
- net['is_inhib'].append(W_range[0]<0.0 and W_range[1]<=0.0)
- else:
- net['I'][Iindex] = torch.concat((net['I'][Iindex],
- torch.unsqueeze(torch.zeros(nrow, device=device),0)),0)
+ # Calculate mean and std for normal distributions
+ Wmn = (W_range[0]+W_range[1])/2.0
+ Wsd = (W_range[1]-W_range[0])/6.0
- # remove connections based on calculated indexes
- if setzeroExc.any():
- net['W'][excIndex][n,:,:][setzeroExc] = 0.0 # excitatory connections
- if setzeroInh.any():
- net['W'][inhIndex][n,:,:][setzeroInh] = 0.0 # inhibitory connections
-
- # Normalise the weights (except fast inhib weights)
- nrmExc = torch.linalg.norm(net['W'][excIndex][len(net['W'][excIndex])-1],ord=1,axis=0)
- nrmInh = torch.linalg.norm(net['W'][inhIndex][len(net['W'][inhIndex])-1],ord=1,axis=0)
- nrmExc[nrmExc==0.0] = 1.0
- nrmInh[nrmInh==0.0] = 1.0
- net['W'][excIndex][n] = net['W'][excIndex][n,:,:]/nrmExc
- net['W'][inhIndex][n] = net['W'][inhIndex][n,:,:]/nrmInh
-
-##################################
-# Normalise all the firing rates
-# net: BITnet instance
-
-def norm_rates(net):
-
- for i,rate in enumerate(net['fire_rate']):
- if rate.any() and net['eta_ip'][i] > 0.0:
- net['thr'][i] = net['thr'][i] + net['eta_ip'][i]*(net['x'][i]-rate)
- net['thr'][i][net['thr'][i]<0.0] = 0.0
-
-##################################
-# Normalise inhib weights to balance input currents
-# net: BITnet instance
-
-def norm_inhib(net):
-
- for i,W in enumerate(net['W']):
- if net['eta_stdp'][i] < 0:
- lyr = net['W_lyr'][len(net['W_lyr'])-1]
- try:
- wadj = (net['x_input'][lyr[1]]*W)*-net['eta_stdp'][i]*50
- net['W'][i] += wadj
- net['W'][i][W>0.0] = -0.000001
- except RuntimeWarning:
- print("norm_inhib err")
- pdb.set_trace()
-
-##################################
-# Propagate spikes thru the network
-# net: SORN instance
-def add_spikesTest(net,device,dims):
-
- # Start with the constant input in the neurons of each layer
- for i,const in enumerate(net['const_inp']):
-
- net['x_input'][i] = torch.full_like(net['x_input'][i],0.0)
- net['x_input'][i] += net['const_inp'][i]
- # Find the threshold crossings (overwritten later if needed)
+ # Initialize weights as empty tensors
+ W = torch.empty((0, nrow, ncol), device=device)
+
+ # Normally disribute random weights
+ W = torch.empty(nrow, ncol, device=device).normal_(mean=Wmn, std=Wsd)
- net['x'][i] = torch.clamp((net['x_input'][i]-net['thr'][i]),0.0,0.9)
-
- # insert any predefined spikes
- start = dims * (net['step_num'] - 1)
- end = dims * net['step_num']
- for i in range(len(net['set_spks'])):
- if len(net['set_spks'][i]): # detect if any set_spks tensors
- start = dims * (net['step_num'] - 1)
- end = dims * net['step_num']
- index = torch.arange(start,end,device=device,dtype=int)
- if i == len(net['set_spks'])-1: # spike forcing
- net['x'][i] = net['set_spks'][i].index_fill_(-1,index,0.5)
- else:
- net['x'][i] = torch.index_select(net['set_spks'][i],-1,index)
-
-
-
-def add_spikes(net,device,dims):
-
- # Start with the constant input in the neurons of each layer
- for i,const in enumerate(net['const_inp']):
- if len(net['W_lyr']) > 1 and i == 1:
- net['x_input'][i] = net['x_feat'][net['step_num']-1]
+ # Remove inappropriate weights based on sign from W_range
+ if W_range[-1] != 0:
+ # For excitatory weights
+ W[W < 0] = 0.0
else:
- net['x_input'][i] = torch.full_like(net['x_input'][i],0.0)
- net['x_input'][i] += net['const_inp'][i]
- # Find the threshold crossings (overwritten later if needed)
-
- net['x'][i] = torch.clamp((net['x_input'][i]-net['thr'][i]),0.0,0.9)
+ # For inhibitory weights
+ W[W > 0] = 0.0
- # insert any predefined spikes
- start = dims * (net['step_num'] - 1)
- end = dims * net['step_num']
- for i in range(len(net['set_spks'])):
- if len(net['set_spks'][i]): # detect if any set_spks tensors
- start = dims * (net['step_num'] - 1)
- end = dims * net['step_num']
- index = torch.arange(start,end,device=device,dtype=int)
- if i == len(net['set_spks'])-1: # spike forcing
- net['x'][i] = net['set_spks'][i].index_fill_(-1,index,0.5)
- else:
- net['x'][i] = torch.index_select(net['set_spks'][i],-1,index)
-
-def calc_spikes(net,layersInfo):
-
- # get layer index information
- excW = layersInfo[0]
- inhW = layersInfo[1]
- layers = net['W_lyr'][layersInfo[2]]
-
- # Synaptic currents last for 1 timestep
- # excitatory weights
- net['I'][layers[0]] = torch.bmm(net['x'][layers[0]],net['W'][excW])
- net['x_input'][layers[1]] += net['I'][layers[0]]
- #inhibitory weights
- net['I'][layers[0]] = torch.bmm(net['x'][layers[0]],net['W'][inhW])
- net['x_input'][layers[1]] += net['I'][layers[0]]
+ # Remove weights based on connection probability
+ setzero = np.random.rand(nrow,ncol) > p
+ if setzero.any():
+ W[setzero] = 0.0
- # adjust the spikes based on threshold
- if not len(net['set_spks'][layers[1]]): # No predefined spikes for this layer
- net['x'][layers[1]] = torch.clamp((net['x_input'][layers[1]]-net['thr'][layers[1]]),
- min=0.0,max=0.9)
- else: # Predefined spikes exist for this layer, remember the calculated ones
- net['x_calc'][layers[1]] = torch.clamp((net['x_input'][layers[1]]-net['thr'][layers[1]]),
- min=0.0,max=0.9)
+ # Normalise the weights
+ nrm = torch.linalg.norm(W[len(W)-1],ord=1,axis=0)
+ nrm[nrm==0.0] = 1.0
+ W = nn.Parameter(W/nrm)
- # update the x previous variable with calculated spikes
- net['x_prev'][layers[1]] = net['x'][layers[1]]
-
- # Finally, update mean firing rates and record all spikes if needed
- for i,eta in enumerate(net['eta_ip']):
+ return W
- if net['rec_spks'][i]:
- outspk = (net['x'][i][0,0,:]).detach().cpu().numpy() # detach to numpy for easy plotting
- n_idx = np.nonzero(outspk)
- net['spikes'][i].extend([net['step_num']+net['x'][i][0,:,:].detach().cpu().numpy(),n]
- for n in n_idx)
-
-##################################
-# Calculate STDP
-# net: BITnet instance
-
-def calc_stdp(net,layerInfo):
+def add_input(spikes, layer):
+
+ # Add the constant input
+ spikes += layer.const_inp
- # get weight layer information
- excW = layerInfo[0]
- inhW = layerInfo[1]
- layers = net['W_lyr'][layerInfo[2]]
- shape = list(net['W'][excW].size())
+ return spikes
- #
- # Spike Forcing has special rules to make calculated and forced spikes match
- #
- if len(net['set_spks'][layers[1]]): # will run for the output layer
+def clamp_spikes(spikes, layer):
+ # Clamp outputs between 0 and 0.9 after subtracting thresholds from input
+ spikes = torch.clamp(torch.sub(spikes, layer.thr), min=0.0, max=0.9)
+
+ return spikes
- # Diff between forced and calculated spikes
- xdiff = net['x'][layers[1]] - net['x_calc'][layers[1]]
+def calc_stdp(prespike, spikes, noclp, layer, idx, prev_layer=None):
+ # Spike Forcing has special rules to make calculated and forced spikes match
+ if layer.spk_force:
+
+ # Get layer dimensions
+ shape = layer.w.weight.data.shape
+
+ # Get the output neuron index
+ idx_sel = torch.arange(int(idx[0]), int(idx[0]) + 1,
+ device=layer.device,
+ dtype=int)
- # Threshold rules - lower it if calced spike is smaller (and vice versa)
- net['thr'][layers[1]] -= torch.sign(xdiff)*torch.abs(torch.tensor(net['eta_stdp'][excW]))/10
- net['thr'][layers[1]] -= torch.sign(xdiff)*torch.abs(torch.tensor(net['eta_stdp'][inhW]))/10
- net['thr'][layers[1]][net['thr'][layers[1]]<0.0] = 0.0 # don't go -ve
+ # Difference between forced and calculated spikes
+ layer.x = torch.full_like(layer.x, 0)
+ xdiff = layer.x.index_fill_(-1, idx_sel, 0.5) - spikes
+ xdiff.clamp(min=0.0, max=0.9)
# Pre and Post spikes tiled across and down for all synapses
- if net['have_rate'][layers[0]]:
- # Modulate learning rate by firing rate (low firing rate = high learning rate)
- mpre = net['x'][layers[0]]/net['fire_rate'][layers[0]]
+ if prev_layer.fire_rate == None:
+ mpre = prespike
else:
- mpre = net['x'][layers[0]]
- pre = torch.tile(torch.reshape(mpre,(shape[0],shape[1],1)),(1,shape[2]))
- post = torch.tile(xdiff,(shape[1],1))
-
- # Excitatory connections
- if net['eta_stdp'][excW] > 0:
- havconn = net['W'][excW]>0
- inc_stdp_exc = pre*post*havconn
- # Inhibitory connections
- if net['eta_stdp'][inhW] < 0:
- havconn = net['W'][inhW]<0
- inc_stdp_inh = -pre*post*havconn
+ # Modulate learning rate by firing rate (low firing rate = high learning rate)
+ mpre = prespike/prev_layer.fire_rate
+
+ # Tile out pre- and post- spikes for STDP weight updates
+ pre = torch.tile(torch.reshape(mpre, (shape[1], 1)), (1, shape[0]))
+ post = torch.tile(xdiff, (shape[1], 1))
# Apply the weight changes
- net['W'][excW] += inc_stdp_exc*net['eta_stdp'][excW]
- net['W'][inhW] += inc_stdp_inh*net['eta_stdp'][inhW]
-
- #
+ layer.w.weight.data += ((pre * post * layer.havconnCombinedExc.T) *
+ layer.eta_stdp).T
+ layer.w.weight.data += ((-pre * post * layer.havconnCombinedInh.T) *
+ (layer.eta_stdp * -1)).T
+
# Normal STDP
- #
else:
- # tile out pre- and post-spikes
- pre = torch.tile(torch.reshape(net['x'][layers[0]],(shape[0],shape[1],1)),(1,shape[2]))
- post = torch.tile(net['x'][layers[1]],(shape[1],1))
-
- # Excitatory synapses
- havconnExc = net['W'][excW]>0
- inc_stdpExc = (0.5-post)*(pre>0)*(post>0)*havconnExc
- # Inhibitory synapses
- havconnInh = net['W'][inhW]<0
- inc_stdpInh = (0.5-post)*(pre>0)*(post>0)*havconnInh
-
- # Apply the weight changes
- net['W'][excW] += inc_stdpExc*net['eta_stdp'][excW]
- net['W'][inhW] += inc_stdpInh*net['eta_stdp'][inhW]
+ # Get layer dimensions
+ shape = layer.w.weight.data.shape
- # fix connection weights if too +ve or -ve
- # Excitation - pruning and synaptogenesis (structural plasticity)
- net['W'][excW][net['W'][excW]<0.0] = 0.000001
- net['W'][excW][net['W'][excW]>10.0] = 10.0
- # Inhibition - must not go +ve
- net['W'][inhW][net['W'][inhW]>0.0] = -0.000001
- net['W'][inhW][net['W'][inhW]<-10.0] = -10.0
-
-##################################
-# Run the simulation
-# net: BITnet instance
-# n_steps: number of steps
-
-def runSim(net,n_steps,device,layers):
-
- # Inc step count
- net['step_num'] += 1
- # Propagate spikes from pre to post neurons
- add_spikes(net,device,net['spike_dims'])
- calc_spikes(net,layers)
-
- # Calculate STDP weight changes
- calc_stdp(net,layers)
-
- # Normalise firing rates and inhibitory balance
- norm_rates(net)
- norm_inhib(net)
+ # Tile out pre- and post-spikes
+ pre = torch.tile(torch.reshape(prespike, (shape[1], 1)), (1, shape[0]))
+ post = torch.tile(spikes, (shape[1], 1))
-def testSim(net,device):
-
- net['step_num'] += 1
- add_spikesTest(net,device,net['spike_dims'])
- for n in range(int(len(net['W'])/2)): # run loop for number of layers
- layers = [int(0 + (n*2)), int(1 + (n*2)), int(0 + n)]
- calc_spikes(net,layers)
-
-##################################
-# Plot recorded spikes in current subplot
-# net: BITnet instance
-
-def subplotSpikes(net,cutoff):
-
- n_tot = 0
- for i,sp in enumerate(net['spikes']):
- x=[]; y=[]
- for n in sp:
- x.extend(list(n[0]))
- y.extend(list(n[1]+n_tot))
-
- plt.plot(x,y,'.',ms=1)
- n_tot += np.size(net['x'][i].detach().cpu().numpy())
+ # Apply positive and negative weight changes
+ layer.w.weight.data += (((0.5 - post) * (pre > 0) * (post > 0) *
+ layer.havconnCombinedExc.T) * layer.eta_stdp).T
+ layer.w.weight.data += (((0.5 - post) * (pre > 0) *
+ (post > 0) * layer.havconnCombinedInh.T) * (layer.eta_stdp * -1)).T
+
+ # Remove negative weights for excW and positive for inhW
+ layer.w.weight.data[layer.havconnCombinedExc] = layer.w.weight.data[layer.havconnCombinedExc].clamp(min=1e-06, max=10)
+ layer.w.weight.data[layer.havconnCombinedInh] = layer.w.weight.data[layer.havconnCombinedInh].clamp(min=-10, max=-1e-06)
+
+ # Check if layer has target firing rate and an ITP learning rate
+ if layer.have_rate and layer.eta_ip > 0.0:
+
+ # Replace the original layer.thr with the updated one
+ layer.thr.data += layer.eta_ip * (layer.x - layer.fire_rate)
+ layer.thr.data[layer.thr.data < 0] = 0
-##################################
-# Plot recorded spikes in new figure
-# net: BITnet instance
-
-def plotSpikes(net,cutoff):
+ # Check if layer has inhibitory weights and an stdp learning rate
+ if torch.any(layer.w.weight.data).item() and layer.eta_stdp != 0:
+
+ # Normalize the inhibitory weights using homeostasis
+ inhW = layer.w.weight.data.T.clone()
+ inhW[inhW>0] = 0
+ layer.w.weight.data += (torch.mul(noclp,inhW) * layer.eta_stdp*50).T
+ #layer.w.weight.data[layer.w.weight.data > 0.0] = -1e-06
- plt.figure()
- subplotSpikes(net,cutoff)
- plt.show(block=False)
-
-##################################
+ return layer
\ No newline at end of file
diff --git a/src/dataset.py b/src/dataset.py
new file mode 100644
index 0000000..9ed9112
--- /dev/null
+++ b/src/dataset.py
@@ -0,0 +1,208 @@
+import os
+import math
+import torch
+
+import pandas as pd
+import numpy as np
+import torch.nn.functional as F
+import torch.quantization as tq
+
+from torchvision.io import read_image
+from torch.utils.data import Dataset
+
+class GetPatches2D:
+ def __init__(self, patch_size, image_pad):
+ self.patch_size = patch_size
+ self.image_pad = image_pad
+
+ def __call__(self, img):
+
+ # Assuming image_pad is already a PyTorch tensor. If not, you can convert it:
+ # image_pad = torch.tensor(image_pad).to(torch.float64)
+
+ # Using unfold to get 2D sliding windows.
+ unfolded = self.image_pad.unfold(0, self.patch_size[0], 1).unfold(1, self.patch_size[1], 1)
+ # The size of unfolded will be [nrows, ncols, patch_size[0], patch_size[1]]
+
+ # Reshaping the tensor to the desired shape
+ patches = unfolded.permute(2, 3, 0, 1).contiguous().view(self.patch_size[0]*self.patch_size[1], -1)
+
+ return patches
+
+
+class PatchNormalisePad:
+ def __init__(self, patches):
+ self.patches = patches
+
+
+ def nanstd(self,input_tensor, dim=None, unbiased=True):
+ if dim is not None:
+ valid_count = torch.sum(~torch.isnan(input_tensor), dim=dim, dtype=torch.float)
+ mean = torch.nansum(input_tensor, dim=dim) / valid_count
+ diff = input_tensor - mean.unsqueeze(dim)
+ variance = torch.nansum(diff * diff, dim=dim) / valid_count
+
+ # Bessel's correction for unbiased estimation
+ if unbiased:
+ variance = variance * (valid_count / (valid_count - 1))
+ else:
+ valid_count = torch.sum(~torch.isnan(input_tensor), dtype=torch.float)
+ mean = torch.nansum(input_tensor) / valid_count
+ diff = input_tensor - mean
+ variance = torch.nansum(diff * diff) / valid_count
+
+ # Bessel's correction for unbiased estimation
+ if unbiased:
+ variance = variance * (valid_count / (valid_count - 1))
+
+ return torch.sqrt(variance)
+
+ def __call__(self, img):
+ img = torch.squeeze(img,0)
+ patch_size = (self.patches, self.patches)
+ patch_half_size = [int((p-1)/2) for p in patch_size ]
+
+ # Compute the padding. If patch_half_size is a scalar, the same value will be used for all sides.
+ if isinstance(patch_half_size, int):
+ pad = (patch_half_size, patch_half_size, patch_half_size, patch_half_size) # left, right, top, bottom
+ else:
+ # If patch_half_size is a tuple, then we'll assume it's in the format (height, width)
+ pad = (patch_half_size[1], patch_half_size[1], patch_half_size[0], patch_half_size[0]) # left, right, top, bottom
+
+ # Apply padding
+ image_pad = F.pad(img, pad, mode='constant', value=float('nan'))
+
+ nrows = img.shape[0]
+ ncols = img.shape[1]
+ patcher = GetPatches2D(patch_size,image_pad)
+ patches = patcher(img)
+ mus = torch.nanmean(patches, dim=0)
+ stds = self.nanstd(patches, dim=0)
+ with np.errstate(divide='ignore', invalid='ignore'):
+ im_norm = (img - mus.reshape(nrows, ncols)) / stds.reshape(nrows, ncols)
+
+ im_norm[torch.isnan(im_norm)] = 0.0
+ im_norm[im_norm < -1.0] = -1.0
+ im_norm[im_norm > 1.0] = 1.0
+
+ return im_norm
+
+class SetImageAsSpikes:
+ def __init__(self, intensity=255, test=True):
+ self.intensity = intensity
+
+ # Setup QAT FakeQuantize for the activations (your spikes)
+ self.fake_quantize = torch.quantization.FakeQuantize(
+ observer=torch.quantization.MovingAverageMinMaxObserver,
+ quant_min=0,
+ quant_max=255,
+ dtype=torch.quint8,
+ qscheme=torch.per_tensor_affine,
+ reduce_range=False
+ )
+
+ def train(self):
+ self.fake_quantize.train()
+
+ def eval(self):
+ self.fake_quantize.eval()
+
+ def __call__(self, img_tensor):
+ N, W, H = img_tensor.shape
+ reshaped_batch = img_tensor.view(N, 1, -1)
+
+ # Divide all pixel values by 255
+ normalized_batch = reshaped_batch / self.intensity
+ normalized_batch = torch.squeeze(normalized_batch, 0)
+
+ # Apply FakeQuantize
+ spikes = self.fake_quantize(normalized_batch)
+
+ if not self.fake_quantize.training:
+ scale, zero_point = self.fake_quantize.calculate_qparams()
+ spikes = torch.quantize_per_tensor(spikes, float(scale), int(zero_point), dtype=torch.quint8)
+
+ return spikes
+
+class ProcessImage:
+ def __init__(self, dims, patches):
+ self.dims = dims
+ self.patches = patches
+
+ def __call__(self, img):
+ # Convert the image to grayscale using the standard weights for RGB channels
+ if img.shape[0] == 3:
+ img = 0.299 * img[0] + 0.587 * img[1] + 0.114 * img[2]
+ # Add a channel dimension to the resulting grayscale image
+ img= img.unsqueeze(0)
+
+ # gamma correction
+ mid = 0.5
+ mean = torch.mean(img)
+ gamma = math.log(mid * 255) / math.log(mean)
+ img = torch.pow(img, gamma).clip(0, 255)
+
+ # resize and patch normalize
+ if len(img.shape) == 3:
+ img = img.unsqueeze(0)
+ img = F.interpolate(img, size=self.dims, mode='bilinear', align_corners=False)
+ img = img.squeeze(0)
+ patch_normaliser = PatchNormalisePad(self.patches)
+ im_norm = patch_normaliser(img)
+ img = (255.0 * (1 + im_norm) / 2.0).to(dtype=torch.uint8)
+ img = torch.unsqueeze(img,0)
+ spike_maker = SetImageAsSpikes()
+ img = spike_maker(img)
+ img = torch.squeeze(img,0)
+
+ return img
+
+class CustomImageDataset(Dataset):
+ def __init__(self, annotations_file, base_dir, img_dirs, transform=None, target_transform=None,
+ skip=1, max_samples=None, test=True, img_range=None):
+ self.transform = transform
+ self.target_transform = target_transform
+ self.skip = skip
+ self.img_range = img_range
+
+ # Load image labels from each directory, apply the skip and max_samples, and concatenate
+ self.img_labels = []
+ for img_dir in img_dirs:
+
+ img_labels = pd.read_csv(annotations_file)
+ img_labels['file_path'] = img_labels.apply(lambda row: os.path.join(base_dir,img_dir, row.iloc[0]), axis=1)
+ if self.img_range is not None:
+ img_labels = img_labels.iloc[self.img_range[0]:self.img_range[1]+1]
+ # Select specific rows based on the skip parameter
+ img_labels = img_labels.iloc[::skip]
+ # Limit the number of samples to max_samples if specified
+ if max_samples is not None:
+ img_labels = img_labels.iloc[:max_samples]
+
+ # Determine if the images being fed are training or testing
+ if test:
+ self.img_labels = img_labels
+ else:
+ self.img_labels.append(img_labels)
+
+ if isinstance(self.img_labels,list):
+ # Concatenate all the DataFrames
+ self.img_labels = pd.concat(self.img_labels, ignore_index=True)
+
+ def __len__(self):
+ return len(self.img_labels)
+
+ def __getitem__(self, idx):
+ img_path = self.img_labels.iloc[idx]['file_path']
+ if not os.path.exists(img_path):
+ raise FileNotFoundError(f"No file found for index {idx} at {img_path}.")
+
+ image = read_image(img_path)
+ label = self.img_labels.iloc[idx, 1]
+
+ if self.transform:
+ image = self.transform(image)
+ if self.target_transform:
+ label = self.target_transform(label)
+
+ return image, label
\ No newline at end of file
diff --git a/src/loggers.py b/src/loggers.py
new file mode 100644
index 0000000..eace9ef
--- /dev/null
+++ b/src/loggers.py
@@ -0,0 +1,90 @@
+import os
+import torch
+import logging
+
+from datetime import datetime
+
+def model_logger():
+ """
+ Configure the model logger
+ """
+ now = datetime.now()
+ output_folder = './output/' + now.strftime("%d%m%y-%H-%M-%S")
+
+ os.mkdir(output_folder)
+ # Create the logger
+ logger = logging.getLogger("VPRTempo")
+ if (logger.hasHandlers()):
+ logger.handlers.clear()
+ # Set the logger level
+ logger.setLevel(logging.DEBUG)
+ logging.basicConfig(filename=output_folder + "/logfile.log",
+ filemode="a+",
+ format="%(asctime)-15s %(levelname)-8s %(message)s")
+ # Add the logger to the console (if specified)
+ logger.addHandler(logging.StreamHandler())
+
+ logger.info('')
+ logger.info('██╗ ██╗██████╗ ██████╗ ████████╗███████╗███╗ ███╗██████╗ ██████╗')
+ logger.info('██║ ██║██╔══██╗██╔══██╗╚══██╔══╝██╔════╝████╗ ████║██╔══██╗██╔═══██╗')
+ logger.info('██║ ██║██████╔╝██████╔╝ ██║ █████╗ ██╔████╔██║██████╔╝██║ ██║')
+ logger.info('╚██╗ ██╔╝██╔═══╝ ██╔══██╗ ██║ ██╔══╝ ██║╚██╔╝██║██╔═══╝ ██║ ██║')
+ logger.info(' ╚████╔╝ ██║ ██║ ██║ ██║ ███████╗██║ ╚═╝ ██║██║ ╚██████╔╝')
+ logger.info(' ╚═══╝ ╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚══════╝╚═╝ ╚═╝╚═╝ ╚═════╝ ')
+ logger.info('-----------------------------------------------------------------------')
+ logger.info('Temporally Encoded Spiking Neural Network for Visual Place Recognition v1.1.0')
+ logger.info('Queensland University of Technology, Centre for Robotics')
+ logger.info('')
+ logger.info('© 2023 Adam D Hines, Peter G Stratton, Michael Milford, Tobias Fischer')
+ logger.info('MIT license - https://github.com/QVPR/VPRTempo')
+ logger.info('\\\\\\\\\\\\\\\\\\\\\\\\')
+ logger.info('')
+ if torch.cuda.is_available():
+ logger.info('CUDA available: ' + str(torch.cuda.is_available()))
+ current_device = torch.cuda.current_device()
+ logger.info('Current device is: ' + str(torch.cuda.get_device_name(current_device)))
+ else:
+ logger.info('CUDA available: ' + str(torch.cuda.is_available()))
+ logger.info('Current device is: CPU')
+ logger.info('')
+
+def model_logger_quant():
+ """
+ Configure the logger
+ """
+
+ now = datetime.now()
+ output_folder = './output/' + now.strftime("%d%m%y-%H-%M-%S")
+
+ os.mkdir(output_folder)
+ # Create the logger
+ logger = logging.getLogger("VPRTempo")
+ if (logger.hasHandlers()):
+ logger.handlers.clear()
+ # Set the logger level
+ logger.setLevel(logging.DEBUG)
+ logging.basicConfig(filename=output_folder + "/logfile.log",
+ filemode="a+",
+ format="%(asctime)-15s %(levelname)-8s %(message)s")
+ # Add the logger to the console (if specified)
+ logger.addHandler(logging.StreamHandler())
+
+ logger.info('')
+
+ logger.info('██╗ ██╗██████╗ ██████╗ ████████╗███████╗███╗ ███╗██████╗ ██████╗ ██████╗ ██╗ ██╗ █████╗ ███╗ ██╗████████╗')
+ logger.info('██║ ██║██╔══██╗██╔══██╗╚══██╔══╝██╔════╝████╗ ████║██╔══██╗██╔═══██╗ ██╔═══██╗██║ ██║██╔══██╗████╗ ██║╚══██╔══╝')
+ logger.info('██║ ██║██████╔╝██████╔╝ ██║ █████╗ ██╔████╔██║██████╔╝██║ ██║█████╗██║ ██║██║ ██║███████║██╔██╗ ██║ ██║')
+ logger.info('╚██╗ ██╔╝██╔═══╝ ██╔══██╗ ██║ ██╔══╝ ██║╚██╔╝██║██╔═══╝ ██║ ██║╚════╝██║▄▄ ██║██║ ██║██╔══██║██║╚██╗██║ ██║')
+ logger.info(' ╚████╔╝ ██║ ██║ ██║ ██║ ███████╗██║ ╚═╝ ██║██║ ╚██████╔╝ ╚██████╔╝╚██████╔╝██║ ██║██║ ╚████║ ██║')
+ logger.info(' ╚═══╝ ╚═╝ ╚═╝ ╚═╝ ╚═╝ ╚══════╝╚═╝ ╚═╝╚═╝ ╚═════╝ ╚══▀▀═╝ ╚═════╝ ╚═╝ ╚═╝╚═╝ ╚═══╝ ╚═╝')
+ logger.info('-----------------------------------------------------------------------')
+ logger.info('Temporally Encoded Spiking Neural Network for Visual Place Recognition v1.1.0')
+ logger.info('Queensland University of Technology, Centre for Robotics')
+ logger.info('')
+ logger.info('© 2023 Adam D Hines, Peter G Stratton, Michael Milford, Tobias Fischer')
+ logger.info('MIT license - https://github.com/QVPR/VPRTempo')
+ logger.info('\\\\\\\\\\\\\\\\\\\\\\\\')
+ logger.info('')
+ logger.info('Quantization enabled')
+ logger.info('Current device is: CPU')
+ logger.info('')
diff --git a/src/metrics.py b/src/metrics.py
index 11c4ef3..9a85b84 100644
--- a/src/metrics.py
+++ b/src/metrics.py
@@ -149,11 +149,9 @@ def recallAtK(S_in, GThard, GTsoft=None, K=1):
# ensure logical datatype in GT and GTsoft
GT = GThard.astype('bool')
- GTsoft = GTsoft.astype('bool')
# copy S and set elements that are only true in GTsoft to min(S) to ignore them during evaluation
S = S_in.copy()
- S[GTsoft & ~GT] = S.min()
# discard all query images without an actually matching database image
j = GT.sum(0) > 0 # columns with matches
diff --git a/src/nordland.py b/src/nordland.py
index b7d03d2..0c804ec 100644
--- a/src/nordland.py
+++ b/src/nordland.py
@@ -5,108 +5,111 @@
import re
import shutil
import zipfile
+import sys
+sys.path.append('..//dataset')
from os import walk
-# load and sort the file names in order, not by how OS indexes them
-def atoi(text):
- return int(text) if text.isdigit() else text
-
-def natural_keys(text):
- return [ atoi(c) for c in re.split(r'(\d+)', text) ]
-
-# set the base path to the location of the downloaded Nordland datasets
-basePath = ''
-assert(os.path.isdir(basePath)),"Please set the basePath to the location of the downloaded Nordland datasets"
-
-# define the subfolders of the Nordland datasets
-subPath = ["spring_images_train/section1/","spring_images_train/section2/",
- "fall_images_train/section1/","fall_images_train/section2/",
- "winter_images_train/section1/","winter_images_train/section2/",
- "summer_images_train/section1/","summer_images_train/section2/"]
-
-# set the desired output folder for unzipping and organization
-outDir = ''
-assert(os.path.isdir(outDir)),"Please set the outDir to the desired output location for unzipping the Nordland datasets"
-
-# define output paths for the data
-outPath = [os.path.join(outDir,"spring/"),os.path.join(outDir,"fall/"),
- os.path.join(outDir,"winter/"),os.path.join(outDir,"summer/")]
-
-# check for existence of the zip folders, throw exception if missing
-zipNames = ["spring_images_train.zip","fall_images_train.zip",
- "winter_images_train.zip","summer_images_train.zip"]
-for n in zipNames:
- if not os.path.exists(basePath+n):
- raise Exception('Please ensure dataset .zip folders have been downloaded')
-
-# check if nordland data folders have already been unzipped
-zip_flag = []
-for n, ndx in enumerate(range(0,len(subPath),2)):
- print('Unzipping '+zipNames[n])
- if os.path.exists(basePath+subPath[ndx]):
- # check if the folder contains any files
- file_lst = os.listdir(basePath+subPath[ndx])
- # remove folder if it is empty and unzip the data folder
- if len(file_lst) == 0:
- shutil.rmtree(basePath+subPath[ndx].replace('section1/',''))
+def nord_sort():
+ # load and sort the file names in order, not by how OS indexes them
+ def atoi(text):
+ return int(text) if text.isdigit() else text
+
+ def natural_keys(text):
+ return [ atoi(c) for c in re.split(r'(\d+)', text) ]
+
+ # set the base path to the location of the downloaded Nordland datasets
+ basePath = '../dataset/'
+ assert(os.path.isdir(basePath)),"Please set the basePath to the location of the downloaded Nordland datasets"
+
+ # define the subfolders of the Nordland datasets
+ subPath = ["spring_images_train/section1/","spring_images_train/section2/",
+ "fall_images_train/section1/","fall_images_train/section2/",
+ "winter_images_train/section1/","winter_images_train/section2/",
+ "summer_images_train/section1/","summer_images_train/section2/"]
+
+ # set the desired output folder for unzipping and organization
+ outDir = '../dataset/'
+ assert(os.path.isdir(outDir)),"Please set the outDir to the desired output location for unzipping the Nordland datasets"
+
+ # define output paths for the data
+ outPath = [os.path.join(outDir,"spring/"),os.path.join(outDir,"fall/"),
+ os.path.join(outDir,"winter/"),os.path.join(outDir,"summer/")]
+
+ # check for existence of the zip folders, throw exception if missing
+ zipNames = ["spring_images_train.zip","fall_images_train.zip",
+ "winter_images_train.zip","summer_images_train.zip"]
+ for n in zipNames:
+ if not os.path.exists(basePath+n):
+ raise Exception('Please ensure dataset .zip folders have been downloaded')
+
+ # check if nordland data folders have already been unzipped
+ zip_flag = []
+ for n, ndx in enumerate(range(0,len(subPath),2)):
+ print('Unzipping '+zipNames[n])
+ if os.path.exists(basePath+subPath[ndx]):
+ # check if the folder contains any files
+ file_lst = os.listdir(basePath+subPath[ndx])
+ # remove folder if it is empty and unzip the data folder
+ if len(file_lst) == 0:
+ shutil.rmtree(basePath+subPath[ndx].replace('section1/',''))
+ with zipfile.ZipFile(basePath+zipNames[n],"r") as zip_ref:
+ zip_ref.extractall(basePath)
+ else:
with zipfile.ZipFile(basePath+zipNames[n],"r") as zip_ref:
zip_ref.extractall(basePath)
- else:
- with zipfile.ZipFile(basePath+zipNames[n],"r") as zip_ref:
- zip_ref.extractall(basePath)
-
-# load image paths
-tempPaths = []
-imgPaths = []
-for n in range(0,len(subPath)):
+
+ # load image paths
tempPaths = []
- for (path, dir_names, file_names) in walk(basePath+subPath[n]):
- tempPaths.extend(file_names)
- # sort image names
- tempPaths.sort(key=natural_keys)
- tempPaths = [basePath+subPath[n]+s for s in tempPaths]
- imgPaths = imgPaths + tempPaths
-
-# if output folders already exist, delete them
-for n in outPath:
- if os.path.exists(n):
- shutil.rmtree(n)
- print('Removed pre-existing output folder')
+ imgPaths = []
+ for n in range(0,len(subPath)):
+ tempPaths = []
+ for (path, dir_names, file_names) in walk(basePath+subPath[n]):
+ tempPaths.extend(file_names)
+ # sort image names
+ tempPaths.sort(key=natural_keys)
+ tempPaths = [basePath+subPath[n]+s for s in tempPaths]
+ imgPaths = imgPaths + tempPaths
-# rename and move the training data to match the nordland_imageNames.txt file
-for n in outPath:
- os.mkdir(n)
-for n, filename in enumerate(imgPaths):
- base = os.path.basename(filename)
- split_base = os.path.splitext(base)
- if int(split_base[0]) < 10:
- my_dest = "images-0000"+split_base[0] + ".png"
- elif int(split_base[0]) < 100:
- my_dest = "images-000"+split_base[0] + ".png"
- elif int(split_base[0]) < 1000:
- my_dest = "images-00"+split_base[0] + ".png"
- elif int(split_base[0]) < 10000:
- my_dest = "images-0"+split_base[0] + ".png"
- else:
- my_dest = "images-"+split_base[0] + ".png"
- if "spring" in filename:
- out = outPath[0]
- elif "fall" in filename:
- out = outPath[1]
- elif "winter" in filename:
- out = outPath[2]
- else:
- out = outPath[-1]
-
- fileDest = out + my_dest
- os.rename(filename, fileDest)
-
-# remove the empty folders
-for n, ndx in enumerate(subPath):
- if n%2 == 0:
- shutil.rmtree(basePath+ndx.replace('section1/',''))
- else:
- continue
-
-print('Finished unzipping and organizing Nordland dataset')
\ No newline at end of file
+ # if output folders already exist, delete them
+ for n in outPath:
+ if os.path.exists(n):
+ shutil.rmtree(n)
+ print('Removed pre-existing output folder')
+
+ # rename and move the training data to match the nordland_imageNames.txt file
+ for n in outPath:
+ os.mkdir(n)
+ for n, filename in enumerate(imgPaths):
+ base = os.path.basename(filename)
+ split_base = os.path.splitext(base)
+ if int(split_base[0]) < 10:
+ my_dest = "images-0000"+split_base[0] + ".png"
+ elif int(split_base[0]) < 100:
+ my_dest = "images-000"+split_base[0] + ".png"
+ elif int(split_base[0]) < 1000:
+ my_dest = "images-00"+split_base[0] + ".png"
+ elif int(split_base[0]) < 10000:
+ my_dest = "images-0"+split_base[0] + ".png"
+ else:
+ my_dest = "images-"+split_base[0] + ".png"
+ if "spring" in filename:
+ out = outPath[0]
+ elif "fall" in filename:
+ out = outPath[1]
+ elif "winter" in filename:
+ out = outPath[2]
+ else:
+ out = outPath[-1]
+
+ fileDest = out + my_dest
+ os.rename(filename, fileDest)
+
+ # remove the empty folders
+ for n, ndx in enumerate(subPath):
+ if n%2 == 0:
+ shutil.rmtree(basePath+ndx.replace('section1/',''))
+ else:
+ continue
+
+ print('Finished unzipping and organizing Nordland dataset')
\ No newline at end of file
diff --git a/src/utils.py b/src/utils.py
deleted file mode 100644
index 2a739a5..0000000
--- a/src/utils.py
+++ /dev/null
@@ -1,369 +0,0 @@
-#MIT License
-
-#Copyright (c) 2023 Adam Hines, Peter G Stratton, Michael Milford, Tobias Fischer
-
-#Permission is hereby granted, free of charge, to any person obtaining a copy
-#of this software and associated documentation files (the "Software"), to deal
-#in the Software without restriction, including without limitation the rights
-#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-#copies of the Software, and to permit persons to whom the Software is
-#furnished to do so, subject to the following conditions:
-
-#The above copyright notice and this permission notice shall be included in all
-#copies or substantial portions of the Software.
-
-#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-#SOFTWARE.
-
-# Get the 2D patches or the patch normalization
-
-'''
-Imports
-'''
-import cv2
-import os
-import math
-import torch
-
-import numpy as np
-import matplotlib.pyplot as plt
-import utils as ut
-import blitnet as bn
-
-from metrics import recallAtK, createPR
-from timeit import default_timer
-
-
-def get_patches2D(patch_size,image_pad):
-
- if patch_size[0] % 2 == 0:
- nrows = image_pad.shape[0] - patch_size[0] + 2
- ncols = image_pad.shape[1] - patch_size[1] + 2
- else:
- nrows = image_pad.shape[0] - patch_size[0] + 1
- ncols = image_pad.shape[1] - patch_size[1] + 1
- patches = np.lib.stride_tricks.as_strided(image_pad , patch_size + (nrows, ncols),
- image_pad.strides + image_pad.strides).reshape(patch_size[0]*patch_size[1],-1)
-
- return patches
-
-# Run patch normalization on imported RGB images
-def patch_normalise_pad(img,patches):
-
- patch_size = (patches, patches)
- patch_half_size = [int((p-1)/2) for p in patch_size ]
-
- image_pad = np.pad(np.float64(img), patch_half_size, 'constant',
- constant_values=np.nan)
-
- nrows = img.shape[0]
- ncols = img.shape[1]
- patches = get_patches2D(patch_size,image_pad)
- mus = np.nanmean(patches, 0)
- stds = np.nanstd(patches, 0)
-
- with np.errstate(divide='ignore', invalid='ignore'):
- im_norm = (img - mus.reshape(nrows, ncols)) / stds.reshape(nrows, ncols)
-
- im_norm[np.isnan(im_norm)] = 0.0
- im_norm[im_norm < -1.0] = -1.0
- im_norm[im_norm > 1.0] = 1.0
-
- return im_norm
-
-# Process the loaded images - resize, normalize color, & patch normalize
-def processImage(img,dims,patches):
- # gamma correct images
- mid = 0.5
- mean = np.mean(img)
- gamma = math.log(mid*255)/math.log(mean)
- img = np.power(img,gamma).clip(0,255).astype(np.uint8)
-
- # resize image to 28x28 and patch normalize
- img = cv2.resize(img,(dims[0], dims[1]))
- im_norm = patch_normalise_pad(img,patches)
- img = np.uint8(255.0 * (1 + im_norm) / 2.0)
-
- return img
-
-# Image loader function - runs all image import functions
-def loadImages(test_true,train_paths,img_names,dims,patches,testPath,testLoc):
-
- # get torch device
- device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
- imgs = []
- ids = []
-
- if test_true:
- train_paths = [testPath+testLoc+'/']
-
- if isinstance(train_paths,list):
- for paths in train_paths:
- for m in img_names:
- fullpath = paths+m
- # read and convert image from BGR to RGB
- img = cv2.imread(fullpath)[:,:,::-1]
-
- # convert image
- img = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
- imgProc = processImage(img,dims,patches)
- imgs.append(torch.tensor(imgProc,device=device))
- ids.append(m)
- else:
- for m in img_names:
- fullpath = train_paths+m
- # read and convert image from BGR to RGB
- img = cv2.imread(fullpath)[:,:,::-1]
- # convert image
- img = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
- imgProc = processImage(img,dims,patches)
- imgs.append(torch.tensor(imgProc,device=device))
- ids.append(m)
-
- return imgs, ids
-
-# sets the spike rates from imported images - convert pixel range [0,255] to [0,1]
-# spike rates are set as a 3D tensor with dimensions (m x r x s)
-# m = module
-# r = location repeat
-# s = spikes for number of training images
-def setSpikeRates(data,ids,device,dims,test_true,numImgs,numMods,intensity,locRep):
-
- # output tensor dimensions
- n_input = dims[0] * dims[1]
-
- # Set the spike rates based on the number of example training images
-
- # loop through data and append spike rates for each image
- # organise into 3D tensor based on number of expert modules
- if test_true: # if loading testing data (repeat input across modules)
- init_rates = torch.empty(0,device=device)
- for m in range(numImgs):
-
- init_rates = torch.cat((init_rates, torch.reshape(data[m]/intensity,(n_input,))))
-
- init_rates = torch.unsqueeze(init_rates,0)
- # define the initial spike rates
- for o in range(numMods):
- if o == 0:
- spike_rates = torch.unsqueeze(init_rates,0)
- else:
- spike_rates = torch.concat((spike_rates,torch.unsqueeze(init_rates,0)),0)
-
-
- else: # if loading training data, have separate inputs across modules
- for o in range(numMods):
- start = []
- end = []
- for j in range(locRep):
- mod = j * numImgs
- start.append(int(numImgs/numMods)*(o) + mod)
- end.append(int(numImgs/numMods)*(o + 1) + mod)
-
- # define the initial spike rates for location repeats
- for m in range(locRep):
- rates = torch.empty(0,device=device)
- for jdx, j in enumerate(range(start[m],end[m])):
- rates = torch.cat((rates,
- torch.reshape(data[j]/intensity,(n_input,))),0)
- if m == 0:
- init_rates = torch.unsqueeze(rates,0)
- else:
- init_rates = torch.cat((init_rates,torch.unsqueeze(rates,0)),-1)
-
- # output spike rates into modules
- if o == 0: # append the location repeat initial spikes to a new module
- spike_rates = torch.unsqueeze(init_rates,0)
- else:
- spike_rates = torch.concat((spike_rates,torch.unsqueeze(init_rates,0)),0)
-
- return spike_rates
-
-# plot similarity matrices
-def plot_similarity(mat, name, cmap, ax=None, dpi=600):
-
- if ax is None:
- fig, ax = plt.subplots(dpi=dpi,figsize=(8, 6))
- else:
- fig = ax.get_figure()
-
- cax = ax.matshow(mat, cmap=cmap, aspect='equal')
- fig.colorbar(cax, ax=ax, label="Spike amplitude")
- ax.set_title(name,fontsize = 12)
- ax.set_xlabel("Query",fontsize = 12)
- ax.set_ylabel("Database",fontsize = 12)
-
-
-
-# plot weight matrices
-def plot_weights(W, name, cmap, vmax, dims, ax=None):
- newx = dims[0]
- newy = dims[1]
-
- # loop through expert modules and output weights
- init_weight = np.array([])
- for n in range(len(W[:,0,0])):
- init_weight = np.append(init_weight, np.reshape(W[n,:,:].cpu().numpy(), (dims[2],)))
-
- # reshape the weight matrices
- reshape_weight = np.reshape(init_weight, (newx, newy))
-
- if ax is None:
- fig, ax = plt.subplots()
- else:
- fig = ax.get_figure()
-
- # plot the weight matrix to specified subplot
- cax = ax.matshow(reshape_weight, cmap=cmap, vmin=0, vmax=vmax)
- fig.colorbar(cax, ax=ax, label="Weight strength",shrink=0.5)
-
- # set figure titles and labels
- ax.set_title(name, fontsize = 12)
- ax.set_xlabel("x-weights", fontsize = 12)
- ax.set_ylabel("y-weights", fontsize = 12)
-
-# plot PR curves
-def plot_PR(P, R, name, ax=None):
-
- if ax is None:
- fig, ax = plt.subplots()
- else:
- fig = ax.get_figure()
-
- ax.plot(R, P)
- ax.set_title(name, fontsize=12)
- ax.set_xlabel("Recall", fontsize=12)
- ax.set_ylabel("Precision", fontsize=12)
-
-# plot the recall@N
-def plot_recallN(recallN, N_vals, name, ax=None):
-
- if ax is None:
- fig, ax = plt.subplots()
- else:
- fig = ax.get_figure()
-
- ax.plot(N_vals, recallN)
- ax.set_title(name, fontsize=12)
- ax.set_xlabel("N", fontsize=12)
- ax.set_ylabel("Recall", fontsize=12)
-
-# run recallAtK() function from VPR Tutorial
-def recallAtN(S_in, GThard, GTsoft, N):
-
- # run recall at N over each value of N
- recall_list = []
- for n in N:
- recall_list.append(recallAtK(S_in, GThard, GTsoft, K=n))
-
- return recall_list
-
-def sad(fullTrainPaths, filteredNames, imWidth, imHeight, num_patches, testPath,
- test_location, imgs, ids, number_testing_images, number_training_images,
- validation):
-
- print('')
- print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
- print('Setting up Sum of Absolute Differences (SAD) calculations')
- print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
- print('')
-
- sadcorrect = 0
- # load the training images
- imgs['training'], ids['training'] = ut.loadImages(False,
- fullTrainPaths,
- filteredNames,
- [imWidth,imHeight],
- num_patches,
- testPath,
- test_location)
- del imgs['training'][number_training_images:len(imgs['training'])]
-
- # create database tensor
- for ndx, n in enumerate(imgs['training']):
- if ndx == 0:
- db = torch.unsqueeze(n,0)
- else:
- db = torch.cat((db, torch.unsqueeze(n,0)), 0)
-
- def calc_sad(query, database, const):
- SAD = torch.sum(torch.abs((database * const) - (query * const)), (1,2), keepdim=True)
- for n in range(2):
- SAD = torch.squeeze(SAD,-1)
- return SAD
-
- # calculate SAD for each image to database and count correct number
- imgred = 1/(imWidth * imHeight)
- sad_concat = []
-
- print('Running SAD')
- correctidx = []
- incorrectidx = []
-
- start = default_timer()
- for n, q in enumerate(imgs['testing']):
- pixels = torch.empty([])
-
- # create 3D tensor of query images
- for o in range(number_testing_images):
- if o == 0:
- pixels = torch.unsqueeze(q,0)
- else:
- pixels = torch.cat((pixels,torch.unsqueeze(q,0)),0)
-
- sad_score = calc_sad(pixels, db, imgred)
- best_match = np.argmin(sad_score.cpu().numpy())
-
- if n == best_match:
- sadcorrect += 1
- correctidx.append(n)
- else:
- incorrectidx.append(n)
-
- if validation:
- sad_concat.append(sad_score.cpu().numpy())
-
- end = default_timer()
-
- p100r_local = round((sadcorrect/number_testing_images)*100,2)
- print('')
- print('Sum of absolute differences P@1: '+ str(p100r_local) + '%')
- print('Sum of absolute differences queried at ' + str(round(number_testing_images/(end-start),2)) + 'Hz')
-
- GT = np.zeros((number_testing_images,number_training_images), dtype=int)
- for n in range(len(GT)):
- GT[n,n] = 1
- sad_concat = (1-np.reshape(np.array(sad_concat),(number_training_images,number_testing_images)))
- P,R = createPR(sad_concat,GT,GT,matching="single")
- for n, ndx in enumerate(P):
- P[n] = round(ndx,2)
- R[n] = round(R[n],2)
-
- # make the PR curve
- fig = plt.figure()
- plt.plot(R,P)
- fig.suptitle("Precision Recall curve",fontsize = 12)
- plt.xlabel("Recall",fontsize = 12)
- plt.ylabel("Precision",fontsize = 12)
- plt.show()
-
- # calculate the recall at N
- N_vals = [1,5,10,15,20,25]
- recallN = ut.recallAtN(sad_concat, GT, GT, N_vals)
-
- return P,R,recallN,N_vals
-
-# clear the contents of the weights folder if retraining with same settings
-def clear_weights(training_out):
- if os.path.isfile(training_out + 'net.pkl'):
- os.remove(training_out+'net.pkl')
- if os.path.isfile(training_out + 'GT_imgnames.pkl'):
- os.remove(training_out+'GT_imgnames.pkl')
- if not os.path.isdir(training_out):
- os.mkdir(training_out)
\ No newline at end of file
diff --git a/src/validation.py b/src/validation.py
deleted file mode 100644
index 372dafa..0000000
--- a/src/validation.py
+++ /dev/null
@@ -1,303 +0,0 @@
-#MIT License
-
-#Copyright (c) 2023 Adam Hines, Michael Milford, Tobias Fischer
-
-#Permission is hereby granted, free of charge, to any person obtaining a copy
-#of this software and associated documentation files (the "Software"), to deal
-#in the Software without restriction, including without limitation the rights
-#to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-#copies of the Software, and to permit persons to whom the Software is
-#furnished to do so, subject to the following conditions:
-
-#The above copyright notice and this permission notice shall be included in all
-#copies or substantial portions of the Software.
-
-#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-#FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-#AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-#LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-#OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-#SOFTWARE.
-
-'''
-Imports
-'''
-import os
-import pickle
-import timeit
-import torch
-import math
-
-import utils as ut
-import numpy as np
-import blitnet as bn
-import matplotlib.pyplot as plt
-
-from metrics import createPR
-
-# used to validate the network training, reject network training if threshold not met
-def validate(model):
-
- # unpickle the network
- model.logger.info('Unpickling the network')
- with open(model.training_out+'net.pkl', 'rb') as f:
- net = pickle.load(f)
-
- model.logger.info('Validating network training')
- # set input spikes for training data from one location
- net['set_spks'][0] = ut.setSpikeRates(
- model.imgs['training'][0:model.number_training_images],
- model.ids['training'][0:model.number_training_images],
- model.device,
- [model.imWidth,model.imHeight],
- True,
- model.number_training_images,
- model.number_modules,
- model.intensity,
- model.location_repeat)
-
- # correct place matches variable
- numcorrect = 0
-
- if model.validation:
- outconcat = []
-
- # run the test network on training data to evaluate network performance
- start = timeit.default_timer()
- for t in range(model.number_training_images):
- tonump = np.array([])
- bn.testSim(net, device=model.device)
- # output the index of highest amplitude spike
- tonump = np.append(tonump,
- np.reshape(net['x'][-1].cpu().numpy(),
- [1,1,int(model.number_training_images)]))
- nidx = np.argmax(tonump)
-
- if model.validation:
- outconcat.append(tonump.tolist())
-
- gt_ind = model.filteredNames.index(model.filteredNames[t])
-
- # adjust number of correct matches if GT matches peak output
- if gt_ind == nidx:
- numcorrect += 1
-
- end = timeit.default_timer()
-
- # network must match >75% of training places to be successful
- p100r = (numcorrect/model.number_training_images)*100
- testFlag = (p100r>75)
- if testFlag: # network training was successful
-
- model.logger.info('')
- model.logger.info('Network training successful!')
- model.logger.info('')
-
- model.logger.info('Performance details:')
- model.logger.info("-------------------------------------------")
- model.logger.info('P@100R: '+str(p100r)+
- '% | Query frequency: '+
- str(round(model.number_training_images/(end-start),2))+'Hz')
- model.logger.info('')
-
- # create output folder (if it does not already exist)
- if not os.path.isdir(model.training_out):
- os.mkdir(model.training_out)
- if not os.path.isdir(model.training_out+'images/'):
- os.mkdir(model.training_out+'images/')
- if not os.path.isdir(model.training_out+'images/training/'):
- os.mkdir(model.training_out+'images/training/')
-
- # plot the similarity matrix for network validation
- if model.validation:
- outconcat = np.array(outconcat)
- concatReshape = np.reshape(outconcat,
- (model.number_training_images,
- model.number_training_images))
- folderName = model.output_folder+'/similarity/'
- os.mkdir(folderName)
-
- fig, axes = plt.subplots(1, 1, figsize=(10, 10))
- fig.suptitle('Training similarity', fontsize = 18)
- cmap = plt.cm.gist_yarg
- plot_name = "Similarity: network training validation"
- ut.plot_similarity(concatReshape,
- plot_name,
- cmap,
- ax=axes)
-
- # Reset network details
- net['sspk_idx'] = [0,0,0]
- net['step_num'] = 0
- net['spikes'] = [[],[],[]]
- net['x'] = [[],[],[]]
- net['set_spks'][0] = 0
-
- # plot the weight matrices
- cmap = plt.cm.magma
- cmap_reverse = plt.cm.magma_r
-
- model.logger.info('Plotting weight matrices')
- # make weights folder
- weights_folder = model.output_folder+'/weights/'
- os.mkdir(weights_folder)
-
- # get the maximum weight value for each set
- IF_Exc = torch.max(net['W'][0]).cpu().numpy()
- IF_Inh = torch.min(net['W'][1]).cpu().numpy()
- FO_Exc = torch.max(net['W'][2]).cpu().numpy()
- FO_Inh = torch.min(net['W'][3]).cpu().numpy()
-
- # find highest divisors of weight matrices for plotting
- def closestDivisors(n):
- factor1 = round(math.sqrt(n))
- while n%factor1 > 0: factor1 -= 1
- return factor1, n//factor1
-
- factor1IF, factor2IF = closestDivisors(len(net['W'][0][0][0])*len(net['W'][0][0])*model.number_modules)
- factor1FO, factor2FO = closestDivisors(len(net['W'][2][0][0])*len(net['W'][2][0])*model.number_modules)
-
- # initial weights
- fig, axes = plt.subplots(2, 2, figsize=(10, 10))
- fig.suptitle('Initial weights', fontsize = 18)
-
- ut.plot_weights(W=model.init_weights[0],
- name='I->F Excitatory Weights',
- cmap=cmap,
- vmax=IF_Exc,
- dims=[factor1IF, factor2IF, int((factor1IF*factor2IF)/model.number_modules)],
- ax=axes[0, 0])
-
- ut.plot_weights(W=model.init_weights[1],
- name='I->F Inhibitory Weights',
- cmap=cmap_reverse,
- vmax=IF_Inh,
- dims=[factor1IF, factor2IF, int((factor1IF*factor2IF)/model.number_modules)],
- ax=axes[0, 1])
-
- ut.plot_weights(W=model.init_weights[2],
- name='F->O Excitatory Weights',
- cmap=cmap,
- vmax=FO_Exc,
- dims=[factor1FO, factor2FO, int((factor1FO*factor2FO)/model.number_modules)],
- ax=axes[1, 0])
-
- ut.plot_weights(W=model.init_weights[3],
- name='F->O Inhibitory Weights',
- cmap=cmap_reverse,
- vmax=FO_Inh,
- dims=[factor1FO, factor2FO, int((factor1FO*factor2FO)/model.number_modules)],
- ax=axes[1, 1])
-
-
- plt.tight_layout()
- plt.show()
-
- fig.savefig(weights_folder + 'Combined_Weights.pdf', dpi=600)
-
- # calculated weights
- fig, axes = plt.subplots(2, 2, figsize=(10, 10))
- fig.suptitle('Calculated weights', fontsize = 18)
-
- ut.plot_weights(W=net['W'][0],
- name='I->F Excitatory Weights',
- cmap=cmap,
- vmax=IF_Exc,
- dims=[factor1IF, factor2IF, int((factor1IF*factor2IF)/model.number_modules)],
- ax=axes[0, 0])
-
- ut.plot_weights(W=net['W'][1],
- name='I->F Inhibitory Weights',
- cmap=cmap_reverse,
- vmax=IF_Inh,
- dims=[factor1IF, factor2IF, int((factor1IF*factor2IF)/model.number_modules)],
- ax=axes[0, 1])
-
- ut.plot_weights(W=net['W'][2],
- name='F->O Excitatory Weights',
- cmap=cmap,
- vmax=FO_Exc,
- dims=[factor1FO, factor2FO, int((factor1FO*factor2FO)/model.number_modules)],
- ax=axes[1, 0])
-
- ut.plot_weights(W=net['W'][3],
- name='F->O Inhibitory Weights',
- cmap=cmap_reverse,
- vmax=FO_Inh,
- dims=[factor1FO, factor2FO, int((factor1FO*factor2FO)/model.number_modules)],
- ax=axes[1, 1])
-
-
- plt.tight_layout()
- plt.show()
-
- fig.savefig(weights_folder + 'Combined_Weights.pdf', dpi=600)
-
-
- else:
- # log unsuccessful training details
- model.logger.info('')
- model.logger.info('Network training unsuccessful.')
- model.logger.info('')
- model.logger.info('Performance details:')
- model.logger.info("-------------------------------------------")
- model.logger.info('P@100R: '+str(p100r)+
- '% | Query frequency: '+
- str(round(model.number_training_images/(end-start),2))+'Hz')
- model.logger.info('')
- return testFlag
-
-# run PR and recall at N
-def match_metrics(numpconc, output_folder, number_testing_images, number_training_images, logger):
-
- numpconc = np.array(numpconc).T
-
- # make the output folder
- folderName = output_folder+'/similarity/'
- if not os.path.isdir(folderName):
- os.mkdir(folderName)
-
- # reshape similarity matrix
- sim_mat = np.reshape(numpconc,(number_testing_images, number_training_images))
-
- # generate the ground truth matrix
- GT = np.zeros((number_testing_images, number_training_images), dtype=int)
- for n in range(len(GT)):
- GT[n,n] = 1
-
- # create the main figure
- fig, axes = plt.subplots(2, 2, figsize=(10, 10))
- fig.suptitle('Network metrics', fontsize = 18)
- cmap = plt.cm.tab20c
-
- # plot the similarity matrices
- ut.plot_similarity(sim_mat, 'VPRTempo similarity', cmap, ax=axes[0,0])
- ut.plot_similarity(GT, 'Ground truth', cmap, ax=axes[0,1])
-
- # get the P & R
- P, R = createPR(sim_mat, GT, GT, matching="single")
- for n, ndx in enumerate(P):
- P[n] = round(ndx,2)
- R[n] = round(R[n],2)
-
- logger.info('Precision values: '+str(P))
- logger.info('Recall values: '+str(R))
-
- # plot the PR curve
- ut.plot_PR(P, R, 'Precision-recall curve',ax=axes[1,0])
-
- # calculate the recall at N
- N_vals = [1, 5, 10, 15, 20, 25]
- recallN = ut.recallAtN(sim_mat, GT, GT, N_vals)
-
- # plot the recall at N
- ut.plot_recallN(recallN, N_vals, 'Recall@N',ax=axes[1,1])
-
- # show the full plot
- plt.tight_layout()
- plt.show()
-
- logger.info('')
- for n, ndx in enumerate(recallN):
- logger.info('Recall at N='+str(N_vals[n])+': '+str(round(ndx,2)))
diff --git a/tutorials/.DS_Store b/tutorials/.DS_Store
new file mode 100644
index 0000000..6cbab19
Binary files /dev/null and b/tutorials/.DS_Store differ
diff --git a/tutorials/1_BasicDemo.ipynb b/tutorials/1_BasicDemo.ipynb
new file mode 100644
index 0000000..0aa5780
--- /dev/null
+++ b/tutorials/1_BasicDemo.ipynb
@@ -0,0 +1,602 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "b34c7b8a-e7bb-47f4-b558-be1bde9a7b37",
+ "metadata": {},
+ "source": [
+ "## VPRTempo & VPRTempoQuant - Basic Demo\n",
+ "\n",
+ "### By Adam D Hines (https://research.qut.edu.au/qcr/people/adam-hines/)\n",
+ "\n",
+ "VPRTempo is based on the following paper, if you use or find this code helpful for your research please consider citing the source:\n",
+ " \n",
+ "[Adam D Hines, Peter G Stratton, Michael Milford, & Tobias Fischer. \"VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition. arXiv September 2023](https://arxiv.org/abs/2309.10225)\n",
+ "\n",
+ "### Introduction\n",
+ "\n",
+ "This is a basic, extremely simplified version of VPRTempo that highlights how images are transformed, spikes and weights are used, and the readout for performance using a model trained using our base system and the Quantized Aware Training (QAT) version. This is a basic, extremely simplified version of VPRTempo that highlights how images are transformed, spikes and weights are used, and the readout for performance. Although the proper implementation is in [PyTorch](https://pytorch.org/), we present a simple NumPy example to get started. As in the paper, we will present a simple example using the [Nordland](https://webdiis.unizar.es/~jmfacil/pr-nordland/#download-dataset) dataset with pre-trained set of weights.\n",
+ "\n",
+ "Before starting, make sure the following packages are installed and imported:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c879cd02-82db-441d-9476-fff1925bf494",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Imprt opencv-python, NumPy, and matplotlib.pyplot\n",
+ "try:\n",
+ " import cv2\n",
+ " import numpy as np\n",
+ " import matplotlib.pyplot as plt\n",
+ "except:\n",
+ " !pip3 install numpy, opencv-python, matplotlib # pip install if modules not present\n",
+ " import cv2\n",
+ " import numpy as np\n",
+ " import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "66b11853-6e17-4884-ac92-d35d814add42",
+ "metadata": {},
+ "source": [
+ "Next, we will need to get the pretrained weights for the model. To get them and the other materials for the , we will use [Git Large File Storage](https://git-lfs.com/) to download the required materials. Please ensure you have it downloaded on your local machine prior to running these tutorials."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "be03b081",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import subprocess\n",
+ "\n",
+ "# Run Git LFS pull command\n",
+ "try:\n",
+ " result = subprocess.run(\"git lfs pull\", shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True)\n",
+ " print(\"Git LFS pull successful.\")\n",
+ "except subprocess.CalledProcessError as e:\n",
+ " print(f\"Git LFS pull failed with error:\\n{e.stderr}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bb45df38-e333-46b2-9161-80e6ac367532",
+ "metadata": {},
+ "source": [
+ "### Image processing\n",
+ "\n",
+ "Let's have a look at how we process our images to run through VPRTempo. We utilize a technique called *patch normalization* to resize input images and normalize the pixel intensities. To start, let's see what the original image looks like before patch normalization."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "67f129b5-9a7a-4b50-9d94-b9bf512f8b70",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load the input image\n",
+ "raw_img = cv2.imread('./mats/1_BasicDemo/summer.png')\n",
+ "rgb_img = cv2.cvtColor(raw_img, cv2.COLOR_BGR2RGB) # Convert to RGB\n",
+ "\n",
+ "# Plot the image\n",
+ "plt.imshow(rgb_img)\n",
+ "plt.title('Nordland Summer')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b68cf25e-35ae-4885-9cf1-c1b09ce4ad42",
+ "metadata": {},
+ "source": [
+ "What we have here is a 360x640 RGB image, which for processing through neural networks is too big (230,400 total pixels). So instead, we'll use patch normalization to reduce the image size down to a grayscale 56x56 image to just 3136 pixels in total."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6f67656a-3ba4-4374-b780-4e8bac4ec2d2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load the patch normalized image\n",
+ "patch_img = np.load('./mats/1_BasicDemo/summer_patchnorm.npy', allow_pickle=True)\n",
+ "\n",
+ "# Plot the image\n",
+ "plt.matshow(patch_img)\n",
+ "plt.title('Nordland Summer Patch Normalized')\n",
+ "plt.colorbar(shrink=0.75, label=\"Pixel intensity\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d404dfd2-10bd-4981-96ef-6092a9866fc6",
+ "metadata": {},
+ "source": [
+ "The reduced image dimensions with patch normalization allows for a decent representation of the full scene, despite the smaller size.\n",
+ "\n",
+ "### Convert images to spikes\n",
+ "\n",
+ "'Spikes' in the context of VPRTempo are a little different than conventional spiking neural networks. Typically, spikes from image datasets are converted into Poisson spike trains where the pixel intensity determines the number of spikes to propagate throughout a network. VPRTempo only considers each pixel as a single spike, but considers the *amplitude* of the spike to determine the timing within a single timestep - where large amplitudes (high pixel intensity) spike early in a timestep, and vice versa for small amplitudes. \n",
+ "\n",
+ "Let's flatten the patch normalized image into a 1D-array so we can apply our network weights."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2bd6ae95-2a79-4b45-8a60-503079339739",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Convert 2D image to a 1D-array\n",
+ "patch_1d = np.reshape(patch_img, (3136,))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9d9a5eaf-1de3-461f-b138-3ac820da8bae",
+ "metadata": {},
+ "source": [
+ "### Load the pre-trained network weights\n",
+ "\n",
+ "Our network consists of the following architecture:\n",
+ "\n",
+ " - An input layer sparsely connected to a feature layer, 3136 input neurons to 6272 feature neurons\n",
+ " - The feature layer fully connected to a one-hot-encoded output layer, 6272 feature neurons to 500 output neurons\n",
+ "\n",
+ "Each layer connection is trained separately and stored in different weight matrices for excitatory (positive) and inhibitory (negative) connections. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6d98749a-8f28-477b-871c-93626e96786c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load the input to feature excitatory and inhibitory network weights\n",
+ "featureW = np.load('./mats/1_BasicDemo/featureW.npy')\n",
+ "\n",
+ "# Plot the weights\n",
+ "plt.matshow(featureW.T)\n",
+ "plt.title('Input > Feature Weights')\n",
+ "plt.colorbar(shrink=0.8, label=\"Weight strength\")\n",
+ "\n",
+ "# Display the plots\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "826213d7-7721-440c-b1a8-47fb613339eb",
+ "metadata": {},
+ "source": [
+ "Whilst it might be a little difficult to see, our excitatory connection amplitudes are on average a little higher than our inhibitiory. However, we overall have more inhibitiory connections that positive to balance the system.\n",
+ "\n",
+ "This is because when we set up our connections we use a probability of connections for both excitation and inbhition. In this case, we have a 10% connection probability for excitatory weights and a 50% probability for inhibitiory. This means as well there will be a high number of neurons without connections."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "94acb2f4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# In this function, we will plot and visualize the distribution of weights and connections.\n",
+ "def count_and_plot(array):\n",
+ " # Flatten the 2D array and count positive, negative, and zero values\n",
+ " flattened_array = array.flatten()\n",
+ " positive_count = np.sum(flattened_array > 0)\n",
+ " negative_count = np.sum(flattened_array < 0)\n",
+ " zero_count = np.sum(flattened_array == 0)\n",
+ " \n",
+ " # Calculate percentages\n",
+ " total_count = flattened_array.size\n",
+ " positive_percentage = (positive_count / total_count) * 100\n",
+ " negative_percentage = (negative_count / total_count) * 100\n",
+ " zero_percentage = (zero_count / total_count) * 100\n",
+ "\n",
+ " # Print the results\n",
+ " print(f\"Excitatory Connections: {positive_count} ({positive_percentage:.2f}%)\")\n",
+ " print(f\"Inhibitory Conncetions: {negative_count} ({negative_percentage:.2f}%)\")\n",
+ " print(f\"Zero Connections: {zero_count} ({zero_percentage:.2f}%)\")\n",
+ "\n",
+ " # Create a bar plot of the percentages\n",
+ " categories = ['Excitatory', 'Inhibitory', 'Zero']\n",
+ " percentages = [positive_percentage, negative_percentage, zero_percentage]\n",
+ "\n",
+ " plt.bar(categories, percentages)\n",
+ " plt.xlabel('Category')\n",
+ " plt.ylabel('Percentage')\n",
+ " plt.title('Percentage of Excitatory, Inhibitiory, and Zero Connections')\n",
+ " plt.ylim(0, 60) # Set the y-axis limit to 0-60%\n",
+ " plt.show()\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ "\n",
+ " # Call the function to count and plot\n",
+ " count_and_plot(featureW)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f180220e",
+ "metadata": {},
+ "source": [
+ "Now let's have a look at the feature to the output weights, and see how the distribution of excitiatory and inhibitory connections differs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7609eae5-f584-4c98-9eb6-3c3cf1e2aa04",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load the input to feature excitatory and inhibitory network weights\n",
+ "outputW = np.load('./mats/1_BasicDemo/outputW.npy')\n",
+ "\n",
+ "# Plot the weights\n",
+ "plt.matshow(outputW)\n",
+ "plt.title('Feature > Output Weights')\n",
+ "plt.colorbar(shrink=0.8, label=\"Weight strength\")\n",
+ "\n",
+ "# Display the plots\n",
+ "plt.show()\n",
+ "\n",
+ "# Plot the distributions\n",
+ "count_and_plot(outputW)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d591969a-e72e-43b2-8c89-16a13bb29fe6",
+ "metadata": {},
+ "source": [
+ "### Propagate network spikes\n",
+ "\n",
+ "Now we'll propagate the input spikes across the layers to get the output. All we have to do is multiply the input spikes by the Input > Feature weights for both excitatory and inhibitory matrices and add them, then take the feature spikes and multiply them by the Feature > Output weights and do the smae thing. We'll also clamp spikes in the range of [0, 0.9] to prevent negative spikes and spike explosions.\n",
+ "\n",
+ "Let's do that and visualize the spikes as they're going through, we'll start with the Input to Feature layer."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f6c84239-c176-48c3-8954-25da5f989d61",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Calculate feature spikes (positive and negative weights)\n",
+ "feature_spikes = np.matmul(featureW,patch_1d)\n",
+ "feature_spikes = np.clip(feature_spikes, 0, 0.9)\n",
+ "\n",
+ "# Now create the line plot\n",
+ "plt.plot(np.arange(len(feature_spikes)), feature_spikes)\n",
+ "\n",
+ "# Add title and labels if you wish\n",
+ "plt.title('Feature Layer Spikes')\n",
+ "plt.xlabel('Neuron ID')\n",
+ "plt.ylabel('Spike Amplitude')\n",
+ "\n",
+ "# Show the plot\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4ea0b0a3-66fc-4202-963c-cbd05114d283",
+ "metadata": {},
+ "source": [
+ "This looks a little homogenous, but this is the feature representation of our input image. \n",
+ "\n",
+ "Now let's propagate the feature layer spikes through to the output layer to get our corresponding place match."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f5d4dc99-c7b9-4e9b-ba7c-58f6e30631cb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Calculate output spikes (positive and negative weights)\n",
+ "output_spikes = np.matmul(outputW,feature_spikes)\n",
+ "output_spikes = np.clip(output_spikes, 0, 0.9)\n",
+ "\n",
+ "# Now create the line plot\n",
+ "plt.plot(np.arange(len(output_spikes)), output_spikes)\n",
+ "\n",
+ "# Add title and labels if you wish\n",
+ "plt.title('Output Layer Spikes')\n",
+ "plt.xlabel('Neuron ID')\n",
+ "plt.ylabel('Spike Amplitude')\n",
+ "\n",
+ "# Show the plot\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "54b4f5f6-017b-4d7d-812a-c96baf9cb39f",
+ "metadata": {},
+ "source": [
+ "Success! We have propagated our input spikes across the layers to reach this output. Clearly, one of the output spikes has the highest amplitude. Our network weights were trained on 500 locations from a Fall and Spring traversal of Nordland. For this example, we passed the first location from the Summer traversal through the network to achieve this output - which clearly looks to have spikes Neuron ID '0' the highest!\n",
+ "\n",
+ "Let's prove that."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "780371ca-9dfe-4dd7-857d-e35be73ffd23",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Output the argmax from the output spikes\n",
+ "prediction = np.argmax(output_spikes)\n",
+ "print(f\"Neuron ID with the highest output is {prediction}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0c8c82d7",
+ "metadata": {},
+ "source": [
+ "## Quantized model example\n",
+ "\n",
+ "Now that we have seen how our base model works, let's look at how our int8 quantized model performs by comparison. Working in the in8 space has a few benefits, like faster inferencing time and smaller model sizes. There are a couple differences however when feeding spikes throughout the system that PyTorch performs in the backend.\n",
+ "\n",
+ "Let's start by converting our input image into int8 spikes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5c893e76",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Converting fp32 spikes to int8 uses a learned scale factor during quantization aware training\n",
+ "spike_scale = 133\n",
+ "patch_img_int = patch_img*spike_scale\n",
+ "\n",
+ "# Plot the converted int8 image\n",
+ "plt.matshow(patch_img_int)\n",
+ "plt.title('Nordland Summer Patch Normalized Int8')\n",
+ "plt.colorbar(shrink=0.75, label=\"Pixel intensity\")\n",
+ "plt.show()\n",
+ "\n",
+ "# Convert 2D image to a 1D-array\n",
+ "patch_1d_int = np.reshape(patch_img_int, (3136,))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9f68d3dc",
+ "metadata": {},
+ "source": [
+ "Now we'll load in and plot our integer based weights, as well as some scale factors which will be important to reduce the size of our spikes after multiplying them with our weights."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "70c99f47",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load the scales for the feature and output spikes\n",
+ "feature_scales = np.load('./mats/1_BasicDemo/featureScales.npy',allow_pickle=True)\n",
+ "output_scales = np.load('./mats/1_BasicDemo/outputScales.npy',allow_pickle=True)\n",
+ "\n",
+ "# Load the int8 weights and plot them\n",
+ "featureQuantW = np.load('./mats/1_BasicDemo/featureQuantW.npy')\n",
+ "outputQuantW = np.load('./mats/1_BasicDemo/outputQuantW.npy')\n",
+ "\n",
+ "# Plot the feature weights\n",
+ "plt.matshow(featureQuantW.T)\n",
+ "plt.title('Input > Feature Weights')\n",
+ "plt.colorbar(shrink=0.8, label=\"Weight strength\")\n",
+ "\n",
+ "# Display the plots\n",
+ "plt.show()\n",
+ "\n",
+ "# Plot the output weights\n",
+ "plt.matshow(outputQuantW)\n",
+ "plt.title('Feature > Output Weights')\n",
+ "plt.colorbar(shrink=0.8, label=\"Weight strength\")\n",
+ "\n",
+ "# Display the plots\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f185e3ff",
+ "metadata": {},
+ "source": [
+ "Now as above, let's propagate the input spikes throughout the network."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ddb1ef65",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get the feature spikes\n",
+ "feature_spikes_int = np.matmul(featureQuantW,patch_1d_int)\n",
+ "\n",
+ "# Now create the line plot\n",
+ "plt.plot(np.arange(len(feature_spikes_int)), feature_spikes_int)\n",
+ "\n",
+ "# Add title and labels if you wish\n",
+ "plt.title('Output Layer Spikes')\n",
+ "plt.xlabel('Neuron ID')\n",
+ "plt.ylabel('Spike Amplitude')\n",
+ "\n",
+ "# Show the plot\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6e9189aa",
+ "metadata": {},
+ "source": [
+ "Those are some big spikes! We're going to have to scale these spikes back down before we forward them to the output layer, otherwise we'll have some huge activations. Let's take those scales we loaded in earlier and apply them to the feature spikes.\n",
+ "\n",
+ "We have three things to consider here:\n",
+ " - A slice scale factor (per neuronal connection scale)\n",
+ " - A zero point (a factor to change where 'zero' is)\n",
+ " \n",
+ " Let's print out these three factors and see how they scale our spikes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "95032ac7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Print out the individual scales\n",
+ "print(f\"The slice scale factor is {feature_scales[1]}\")\n",
+ "print(f\"The zero point is {feature_scales[2]}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9f62b909",
+ "metadata": {},
+ "source": [
+ "Now we'll modify and scale our spikes to then pass them on to the feature layer."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9a7dd83d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Scale the feature spikes\n",
+ "scaled_feature_spikes = (feature_spikes_int//(feature_scales[1]))+feature_scales[2]\n",
+ "scaled_feature_spikes = np.clip(scaled_feature_spikes,0,255)\n",
+ "\n",
+ "# Plot the scaled feature spikes\n",
+ "plt.plot(np.arange(len(scaled_feature_spikes)), scaled_feature_spikes)\n",
+ "\n",
+ "# Add title and labels if you wish\n",
+ "plt.title('Output Layer Spikes')\n",
+ "plt.xlabel('Neuron ID')\n",
+ "plt.ylabel('Spike Amplitude')\n",
+ "\n",
+ "# Show the plot\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2a767c92",
+ "metadata": {},
+ "source": [
+ "Now that we've scaled our feature spikes, let's pass them through to the output layer and get our match!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7519c07f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get the output spikes\n",
+ "output_spikes_int = np.matmul(outputQuantW,scaled_feature_spikes)\n",
+ "\n",
+ "# Scale the output spikes\n",
+ "scaled_output_spikes = output_spikes_int//(output_scales[1]) + output_scales[2]\n",
+ "\n",
+ "# Plot the scaled feature spikes\n",
+ "plt.plot(np.arange(len(scaled_output_spikes)), scaled_output_spikes)\n",
+ "\n",
+ "# Add title and labels if you wish\n",
+ "plt.title('Output Layer Spikes')\n",
+ "plt.xlabel('Neuron ID')\n",
+ "plt.ylabel('Spike Amplitude')\n",
+ "\n",
+ "# Show the plot\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3cc4588a",
+ "metadata": {},
+ "source": [
+ "And once again, as in the base model, we can see that output neuron 0 is the highest respondant.\n",
+ "\n",
+ "Let's prove it!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "67e457e4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Output the argmax from the output spikes\n",
+ "prediction = np.argmax(scaled_output_spikes)\n",
+ "print(f\"Neuron ID with the highest output is {prediction}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7bc8a7fb-66b4-455b-922e-b0fdc38b53c5",
+ "metadata": {},
+ "source": [
+ "### Conclusions\n",
+ "\n",
+ "We have gone through a very basic demo of how VPRTempo takes input images, patch normalizes them, and propagates the spikes throughout the weights to achieve the desired matching output. Although this demonstration was performed using NumPy, the torch implementation is virtually the same except we use tensors with or without quantization. \n",
+ "\n",
+ "We also went through how the quantization version of the network handled weights and spikes in the integer domain.\n",
+ "\n",
+ "If you would like to go more in-depth with training and inferencing, checkout some of the [other tutorials](https://github.com/AdamDHines/VPRTempo-quant/tree/main/tutorials) which show you how to train your own model and goes through the more sophisticated implementation of VPRTempo."
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorials/2_Training.ipynb b/tutorials/2_Training.ipynb
new file mode 100644
index 0000000..0479320
--- /dev/null
+++ b/tutorials/2_Training.ipynb
@@ -0,0 +1,199 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "2a09c9e8-68f6-4edd-a8c1-1cc7f6b8bb00",
+ "metadata": {},
+ "source": [
+ "## Training a new VPRTempo & VPRTempoQuant network\n",
+ "\n",
+ "### By Adam D Hines (https://research.qut.edu.au/qcr/people/adam-hines/)\n",
+ "\n",
+ "VPRTempo is based on the following paper, if you use or find this code helpful for your research please consider citing the source:\n",
+ " \n",
+ "[Adam D Hines, Peter G Stratton, Michael Milford, & Tobias Fischer. \"VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition. arXiv September 2023](https://arxiv.org/abs/2309.10225)\n",
+ "\n",
+ "### Introduction\n",
+ "\n",
+ "In this tutorial, we will go through how to train your own model for both the base and quantized version of VPRTempo. \n",
+ "\n",
+ "Before starting, make sure you have [installed the dependencies](https://github.com/AdamDHines/VPRTempo-quant#installation-and-setup) and/or activated the conda environment. You will also need the [Nordland](https://github.com/AdamDHines/VPRTempo-quant#nordland) dataset before proceeding, as this tutorial will cover training the network using this as an example.\n",
+ "\n",
+ "### Training new models for VPRTempo and VPRTempoQuant\n",
+ "\n",
+ "Let's start by training the base model with the default settings (if you have pre-trained a model, it will get removed for the purpose of the tutorial)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a757316e-4aa8-41aa-b03f-1ce4489d3705",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Change the working directory to the main folder from tutorials\n",
+ "import os\n",
+ "os.chdir('../')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eff21590-9d00-4d8f-b860-ca1c0c849187",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Train the base model with the default settings\n",
+ "# If the pre-trained model already exists, we will remove it for the tutorial\n",
+ "file_path = './models/VPRTempo313662725001.pth'\n",
+ "\n",
+ "if os.path.exists(file_path):\n",
+ " os.remove(file_path)\n",
+ " print(\"The file has been deleted.\")\n",
+ "\n",
+ "# Run the training paradigm\n",
+ "!python main.py --train_new_model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "285ca349-6295-4f04-bbe1-360d43111f82",
+ "metadata": {},
+ "source": [
+ "Now we'll run the inferencing model to check and make sure our model trained ok."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1037e4ee-0361-4aa1-bfba-f979ffa51b88",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run the base inferencing network\n",
+ "!python main.py"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cbf8cd37-1af8-420f-ad76-481157a2920e",
+ "metadata": {},
+ "source": [
+ "Great! Now let's have a look at changing a few of the default settings and training different kinds of networks. The default settings train 500 places, so if we want to only look at a smaller number of places we can parse the `--num_places` argument and specify how many places to learn."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "81daf9c8-ea06-4e69-82a5-9179a7c38ea1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Train a new model with 250 places\n",
+ "!python main.py --num_places 250 --train_new_model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a46cde0d-6343-48a4-87f8-aa5c9f56b231",
+ "metadata": {},
+ "source": [
+ "And we can now inference using this smaller model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "559e2391-d0bc-4d58-bb35-8cf69ae08b5e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run the inference for a model with 250 places\n",
+ "!python main.py --num_places 250"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aa835469-90cc-4f09-b7b4-688465321b18",
+ "metadata": {},
+ "source": [
+ "Arguments for the base network work the same for VPRTempoQuant, we just need to also parse the `--quantize` argument. Let's now train another 250 place network, but also change a couple of other parameters. The default VPRTempo settings is a little slow to train on CPU, so let's reduce the image size from 56x56 to 28x28 and change the number of patches for patch normalization."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eee89998-d334-4cdf-ac5b-88aed367ceab",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Train a 250 place network with VPRTempoQuant\n",
+ "!python main.py --quantize --num_places 250 --patches 7 --dims 28,28 --train_new_model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "42d5f3ce-acbb-435d-b197-f7e5841dcd70",
+ "metadata": {},
+ "source": [
+ "And now we can inference this model!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c6a9e948-5349-4714-b7d0-03ce694409b8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run inference on newly trained VPRTempoQuant model\n",
+ "!python main.py --quantize --num_places 250 --patches 7 --dims 28,28"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2648e38e-a0d7-481a-9009-3c4cf3e3fff0",
+ "metadata": {},
+ "source": [
+ "### List of arguments you can parse\n",
+ "\n",
+ "The full list of arguments that can parsed to VPRTempo can be found in the `parse_network` function of `main.py`. Hyperparameters for VPRTempo are hardcoded into the layers and are not recommended to be changed since they generalize fairly well across multiple different datasets. \n",
+ "\n",
+ "### Conclusions\n",
+ "\n",
+ "This tutorial provided a simple overview of how you can train your own models for both VPRTempo and VPRTempoQuant, and changing a few of the network parameters.\n",
+ "\n",
+ "If you would like to go more in-depth, checkout some of the [other tutorials](https://github.com/AdamDHines/VPRTempo-quant/tree/main/tutorials) where we cover how to define your own custom dataset and work with expert modules."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a2bdc6d1-4690-4b48-a75f-f9d716cb7154",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorials/3_Modules.ipynb b/tutorials/3_Modules.ipynb
new file mode 100644
index 0000000..bef58f5
--- /dev/null
+++ b/tutorials/3_Modules.ipynb
@@ -0,0 +1,204 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "7b7b7556-f8df-4e50-801b-7e215e46a415",
+ "metadata": {},
+ "source": [
+ "## Using modules with VPRTempo and VPRTempoQuant\n",
+ "\n",
+ "### By Adam D Hines (https://research.qut.edu.au/qcr/people/adam-hines/)\n",
+ "\n",
+ "VPRTempo is based on the following paper, if you use or find this code helpful for your research please consider citing the source:\n",
+ " \n",
+ "[Adam D Hines, Peter G Stratton, Michael Milford, & Tobias Fischer. \"VPRTempo: A Fast Temporally Encoded Spiking Neural Network for Visual Place Recognition. arXiv September 2023](https://arxiv.org/abs/2309.10225)\n",
+ "\n",
+ "### Introduction\n",
+ "\n",
+ "In this tutorial, we will go through how to use modules with VPRTempo. Modules break up the training data into multiple networks, which has been shown to [improve the overall performance](https://towardsdatascience.com/machine-learning-with-expert-models-a-primer-6c74585f223f) and accuracy of larger models.\n",
+ "\n",
+ "Before starting, make sure you have [installed the dependencies](https://github.com/AdamDHines/VPRTempo-quant#installation-and-setup) and/or activated the conda environment. You will also need the [Nordland](https://github.com/AdamDHines/VPRTempo-quant#nordland) dataset before proceeding, as this tutorial will cover training the network using this as an example.\n",
+ "\n",
+ "### Comparing results using expert modules for VPRTempo\n",
+ "\n",
+ "Let's start by training the base model with 1000 places, which is 500 more than the default settings. We will need to parse the `--train_new_model`, `--num_places`, as well as another argument we haven't seen yet `--max_module`. \n",
+ "\n",
+ "`--max_module` tells the network how many places each expert module should learn, which by default is set to `500`. So if we're training a new, singular network with 1000 places we need to increase `max_module` to 1000."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9e0d1a7b-8788-436a-8dd5-27f60077c524",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Change the working directory to the main folder from tutorials\n",
+ "import os\n",
+ "os.chdir('../')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c0981001-fa1c-49e0-bbe3-e5a628098eba",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Train a single network with 1000 places\n",
+ "!python main.py --num_places 1000 --max_module 1000 --train_new_model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7d8287b-b47b-4f7e-9d1c-4b309746b284",
+ "metadata": {},
+ "source": [
+ "Now let's see how this performs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "32435d47-e583-4768-86ed-2998ec78fdd6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run the inferencing network on the singular 1000 place trained model\n",
+ "!python main.py --num_places 1000 --max_module 1000"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "845764ee-1492-449c-9816-a45ccfe043a1",
+ "metadata": {},
+ "source": [
+ "Performance here is still pretty good, but let's see if we can improve it by splitting up the network into modules!\n",
+ "\n",
+ "Now that splitting up our 1000 place network into 2 networks, we can remove the `--max_module` argument because the default is set to 500. Instead what we will parse is `--num_modules` to tell the network to split things up into two models."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d7cf368b-2f7f-4345-b882-7bf51622c0d6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Train a new 1000 place model with 2 modules\n",
+ "!python main.py --num_places 1000 --num_modules 2 --train_new_model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "84ce9e04-ff62-45ce-83b3-89e4623f0fd1",
+ "metadata": {},
+ "source": [
+ "Now let's see how it compares with the singular model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c6ec3ec7-cca7-45a0-906b-cc3e6d14b5f8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run the network with 2 modules\n",
+ "!python main.py --num_places 1000 --num_modules 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8da07161-81e9-4773-90f1-e9d675e8c071",
+ "metadata": {},
+ "source": [
+ "A modest boost to performance, however you have to imagine how this scales to much larger networks - especially when considering training times. Because the output layer is one-hot encoded, you need to increase the number of output neurons with each place you want to learn. Splitting up networks has a key benefit for VPRTempo to reduce overall training times with little impact on inference speeds. \n",
+ "\n",
+ "(Optional) Run a single network for 2500 places or 5 expert modules for 500 places each (reduced dimensionality to speed things up)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ffd18263-190c-47f4-a37f-130a040d7a67",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Optional: run a 2500 place comparison for singular vs modular networks\n",
+ "# Train networks\n",
+ "!python main.py --num_places 2500 --max_module 2500 --dims 28,28 --patches 7 --train_new_model\n",
+ "!python main.py --num_places 2500 --num_modules 5 --dims 28,28 --patches 7 --train_new_model\n",
+ "# Run inference\n",
+ "!python main.py --num_places 2500 --max_module 2500 --dims 28,28 --patches 7\n",
+ "!python main.py --num_places 2500 --num_modules 5 --dims 28,28 --patches 7"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "02edb3da-9d34-4709-950d-d202c5e902e7",
+ "metadata": {},
+ "source": [
+ "As in the other tutorials, parsing the `--quantize` argument will run exactly the same but for VPRTempoQuant. Let's do a quick comparison."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2e520f0a-7b6f-4546-8169-14f20f2f9d18",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Train networks\n",
+ "#!python main.py --num_places 1500 --max_module 1500 --dims 28,28 --patches 7 --train_new_model --quantize\n",
+ "#!python main.py --num_places 1500 --num_modules 3 --dims 28,28 --patches 7 --train_new_model --quantize\n",
+ "# Run inference\n",
+ "!python main.py --num_places 1500 --max_module 1500 --dims 28,28 --patches 7 --quantize\n",
+ "!python main.py --num_places 1500 --num_modules 3 --dims 28,28 --patches 7 --quantize"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "58581b5c-70a8-4708-bda1-1ccca1edc70b",
+ "metadata": {},
+ "source": [
+ "Once again, we can see that whilst there's a modest boost to the accuracy result the clear improve is the training speed. Because each network is smaller, the opeations on CPU are a lot less computationally heavy when splitting the networks up.\n",
+ "\n",
+ "### Conclusions\n",
+ "\n",
+ "This tutorial provided a simple overview of how you can train network models using expert modules. \n",
+ "\n",
+ "If you would like to go more in-depth, checkout some of the [other tutorials](https://github.com/AdamDHines/VPRTempo-quant/tree/main/tutorials)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0309b80b-05c2-4577-8ea0-2e45bf2d6aef",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.4"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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-Trained networks will appear in this folder as a pickled Python dictionary (.pkl). The naming takes the following format:
-# xxxi - number of neurons in the input (i) layer
-# xxxxf - number of neurons in the feature (f) layer
-# xxxo - number of neurons in the output (o) layer
-# X - number of training epochs
-
-Which will look something like - xxxixxxfxxxoX.pkl
-An example trained network would be - 784i5488f400o4.pkl