Hyper-Networks implementation for Pytorch.

Tested on python 3.8 and pytorch 1.10.

• import it:

from hypernetworks import HyperNetwork

• Create a parent model and a child model (the parent model outputs are the child model parameters/weights)
• Initialize a hyper-network module with a parent model and a child model:

model = HyperNetwork(parent_model, child_model)

• Prepare data that suits a hypernetwork and feed it to the hypernetwork model (x goes through the parent network which outputs weights for the child network. y goes through the child network that get its weights from the parent network's output):

z = model(x, y)

• Notice the hyper-network gets 2 inputs:

child_weights = parent_model(x)
child_model <- child_weights
z = child_model(y)

This code example uses torch_x (torch extensions package):

    batch_size = 6
    num_features = 64
    parent = MLP([num_features, 16, 10], nn.ReLU(), nn.Softmax(dim=1))
    child = MLP([10, 32, 1], nn.ReLU(), nn.Sigmoid())
    x = torch.randn(batch_size, num_features)
    y = torch.randn(3, 10)
    model = HyperNetwork(parent, child)
    z = model(x, y)

• in this example, z size/shape will be (18, 1).

1. HyperNetworks (https://arxiv.org/pdf/1609.09106.pdf)
2. Lior Wolf video on Hyper-Networks (https://www.youtube.com/watch?v=KY9DoutzH6k)