What is the context about the example of pre-trained model usage?

[ValerianRey]

Hi! First of all, thanks for this high quality and open research!

I ran the usage example shown in the README.md:

import torch
import numpy as np

repo = 'OxWearables/ssl-wearables'
harnet5 = torch.hub.load(repo, 'harnet5', class_num=5, pretrained=True)
x = np.random.rand(1, 3, 150)
x = torch.FloatTensor(x)
harnet5(x)

harnet10 = torch.hub.load(repo, 'harnet10', class_num=5, pretrained=True)
x = np.random.rand(1, 3, 300)
x = torch.FloatTensor(x)
harnet10(x)

harnet30 = torch.hub.load(repo, 'harnet30', class_num=5, pretrained=True)
x = np.random.rand(1, 3, 900)
x = torch.FloatTensor(x)
harnet30(x)

I have trouble understanding exactly what the harnet models are. I understand that they are instances of sslearning.models.accNet.Resnet, and my understanding is the following:

• The feature_extractor part was pre-trained using multi-task SSL on UK-Biobank dataset, as explained in your paper
• The classifier part is not trained at all, and we have to train it on any labelled classification dataset (what makes me understand this is that we can select any number class_num).

Is my understanding correct? Also, would it be possible to write this information next to the usage example?

Thanks a lot in advance :)

[angerhang]

@ValerianRey

Thanks for the query. Appreciate your interest in our work.

Your understanding is correct:

• feature_extractor is the pre-trained network.
• classifier is just a task head that supports an arbitrary number of labels.

We are very busy with a few upcoming deadlines right now. If you wish to make a pull request, very happy to take a look and merge it in :D

[ValerianRey]

Thanks for your answer! I made a PR. Hopefully my explanation is accurate enough!