NuzaRelics

The RELICS Batched Dataset Contains NVSS images of the radio relics from the combined catalog of:

Can cluster merger shocks reproduce the luminosity and shape distribution of radio relics? Nuza, Gelszinnis, Hoeft & Yepes, 2017, Monthly Notices of the Royal Astronomical Society, vol. 470, pp. 240-263 arXiv:1704.06661

The RELICS Batched Dataset

The RELICS Batched Dataset contains only 59 objects, all of which are contained in the test batch. These are in random order, but each dataset is accompanied by an identifiable filename containing the coordinates of the source. Each image is 300 x 300 pixels in size, with a standard NVSS pixel size of 15 arcseconds.

Using the Dataset in PyTorch

The nuza.py file contains an instance of the torchvision Dataset() for the RELICS Batched Dataset.

To use it with PyTorch in Python, first import the torchvision datasets and transforms libraries:

from torchvision import datasets
import torchvision.transforms as transforms

Then import the RELICS class:

from nuza import RELICS

Define the transform:

# convert data to a normalized torch.FloatTensor
transform = transforms.Compose([
                                transforms.CenterCrop(32),
                                transforms.ToTensor(),
                                transforms.Normalize([0.5],[0.5]),
                              ])

Read the RELICS dataset:

# choose the training and test datasets
testset = RELICS(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=10, shuffle=True, num_workers=2)

Jupyter Notebooks

An example of notebook iterating the RELICS Dataloader() is provided as a Jupyter notebook.