EquivariantSelfAttention
An implementation of Equivariant e2 convolutional kernals into a convolutional self attention network, applied to radio astronomy data.
This work extends and builds on previous work:
- FR Classification using e2cnn, whose code is here.
- Attention for FR Classificaion, whose code is here. This work makes extensive use of the
e2cnnpackage: e2cnn.
Imports for General Use
import os
import torch
import e2cnn
import pandas as pd
import numpy as np
import configparser as ConfigParser
import utils
Loading a network:
To load a network import the network files, read in a config file, and load the network with the config file parameters:
from networks import AGRadGalNet, VanillaLeNet, testNet, DNSteerableLeNet, DNSteerableAGRadGalNet
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
config = ConfigParser.ConfigParser(allow_no_value=True)
config_path = "configs/CONFIG_NAME"
config.read(config_path)
net = locals()[config['model']['base']](**config['model']).to(device)
Alternatively, if you know which architecture you want to use, only import that and load in the config parameters (i.e. without using locals()):
from networks import DNSteerableAGRadGalNet
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
config = ConfigParser.ConfigParser(allow_no_value=True)
config_path = "configs/CONFIG_NAME"
config.read(config_path)
net = DNSteerableAGRadGalNet(**config['model']).to(device)
Loading a model:
To load the best performing model (according to early stopping) from a given model training session:
path_supliment = config['data']['augment']+'/'
model = utils.utils.load_model(config, load_model='best', device=device, path_supliment=path_supliment)
Testing a model:
Follow the example of evaluation.ipynb for model evaluation.
Defining a model:
To define your own model simply create a new config which follows the examples provided in configs.
Training a model:
To train a model use the train.py using your config file:
python3.8 train.py --config YOUR_CONFIG_NAME.cfg >& logs/YOUR_CONFIG_NAME.log
15 May 23, 2022
91 Dec 12, 2022
56 Nov 29, 2022
1.3k Jan 03, 2023
1.4k Jan 07, 2023
235 Dec 19, 2022
359 Jan 05, 2023
438 Dec 29, 2022
2 Dec 16, 2021
6 Aug 22, 2022
4 Jun 21, 2022
1.5k Dec 30, 2022
41 Dec 05, 2022
1 Nov 19, 2021
396 Dec 01, 2022
129 Dec 30, 2022
18 Dec 05, 2022
121 Sep 26, 2022
42 Dec 05, 2022
4.3k Jan 01, 2023