NIGnets Showcase

# Basic imports
import torch
from torch import nn
import geosimilarity as gs
from NIGnets import NIGnet
from NIGnets.monotonic_nets import SmoothMinMaxNet

from assets.utils import automate_training, plot_curves

class PreAuxNet(nn.Module):
    def __init__(self, layer_count, hidden_dim):
        super().__init__()

        # Pre-Auxilliary net needs closed transform to get same r at theta = 0, 2pi
        self.closed_transform = lambda t: torch.hstack([
            torch.cos(2 * torch.pi * t),
            torch.sin(2 * torch.pi * t)
        ])

        layers = [nn.Linear(2, hidden_dim), nn.BatchNorm1d(hidden_dim), nn.PReLU()]
        for i in range(layer_count):
            layers.append(nn.Linear(hidden_dim, hidden_dim))
            layers.append(nn.BatchNorm1d(hidden_dim))
            layers.append(nn.PReLU())
        layers.append(nn.Linear(hidden_dim, 1))
        layers.append(nn.ReLU())

        self.forward_stack = nn.Sequential(*layers)
    
    def forward(self, t):
        unit_circle = self.closed_transform(t) # Rows are cos(theta), sin(theta)
        r = self.forward_stack(unit_circle)
        x = r * unit_circle # Each row is now r*cos(theta), r*sin(theta)
        return x

from assets.shapes import circle, square

# Generate target curve points
num_pts = 1000
t = torch.linspace(0, 1, num_pts).reshape(-1, 1)
Xt_circle = circle(num_pts)
Xt_square = square(num_pts)

# Initialize networks to learn the target shapes and train
preaux_net = PreAuxNet(layer_count = 2, hidden_dim = 5)
monotonic_net = SmoothMinMaxNet(input_dim = 1, n_groups = 3, nodes_per_group = 3)
circle_net = NIGnet(layer_count = 3, preaux_net = preaux_net, monotonic_net = monotonic_net)
square_net = NIGnet(layer_count = 3, preaux_net = preaux_net, monotonic_net = monotonic_net)

print('Training Circle Net:')
automate_training(
    model = circle_net, loss_fn = gs.MSELoss(), X_train = t, Y_train = Xt_circle,
    learning_rate = 0.1, epochs = 1000, print_cost_every = 200
)
print('Training Square Net:')
automate_training(
    model = square_net, loss_fn = gs.MSELoss(), X_train = t, Y_train = Xt_square,
    learning_rate = 0.1, epochs = 1000, print_cost_every = 200
)

# Get final curve represented by the networks
Xc_circle = circle_net(t)
Xc_square = square_net(t)

# Plot the curves
plot_curves(Xc_circle, Xt_circle)
plot_curves(Xc_square, Xt_square)

from assets.shapes import stanford_bunny

# Generate target curve points
num_pts = 1000
t = torch.linspace(0, 1, num_pts).reshape(-1, 1)
Xt = stanford_bunny(num_pts)

preaux_net = PreAuxNet(layer_count = 2, hidden_dim = 10)
monotonic_net = SmoothMinMaxNet(input_dim = 1, n_groups = 6, nodes_per_group = 6)
nig_net = NIGnet(layer_count = 5, preaux_net = preaux_net, monotonic_net = monotonic_net)

automate_training(
    model = nig_net, loss_fn = gs.MSELoss(), X_train = t, Y_train = Xt,
    learning_rate = 0.1, epochs = 10000, print_cost_every = 2000
)

Xc = nig_net(t)
plot_curves(Xc, Xt)

from assets.shapes import heart

# Generate target curve points
num_pts = 1000
t = torch.linspace(0, 1, num_pts).reshape(-1, 1)
Xt = heart(num_pts)

preaux_net = PreAuxNet(layer_count = 2, hidden_dim = 10)
monotonic_net = SmoothMinMaxNet(input_dim = 1, n_groups = 6, nodes_per_group = 6)
nig_net = NIGnet(layer_count = 3, preaux_net = preaux_net, monotonic_net = monotonic_net)

automate_training(
    model = nig_net, loss_fn = gs.MSELoss(), X_train = t, Y_train = Xt,
    learning_rate = 0.1, epochs = 10000, print_cost_every = 2000
)

Xc = nig_net(t)
plot_curves(Xc, Xt)

from assets.shapes import hand

# Generate target curve points
num_pts = 1000
t = torch.linspace(0, 1, num_pts).reshape(-1, 1)
Xt = hand(num_pts)

preaux_net = PreAuxNet(layer_count = 2, hidden_dim = 10)
monotonic_net = SmoothMinMaxNet(input_dim = 1, n_groups = 10, nodes_per_group = 10)
nig_net = NIGnet(layer_count = 5, preaux_net = preaux_net, monotonic_net = monotonic_net)

automate_training(
    model = nig_net, loss_fn = gs.MSELoss(), X_train = t, Y_train = Xt,
    learning_rate = 0.1, epochs = 10000, print_cost_every = 2000
)

Xc = nig_net(t)
plot_curves(Xc, Xt)

from assets.shapes import puzzle_piece

# Generate target curve points
num_pts = 1000
t = torch.linspace(0, 1, num_pts).reshape(-1, 1)
Xt = puzzle_piece(num_pts)

monotonic_net = SmoothMinMaxNet(input_dim = 1, n_groups = 10, nodes_per_group = 10)
nig_net = NIGnet(layer_count = 5, preaux_net = preaux_net, monotonic_net = monotonic_net)

automate_training(
    model = nig_net, loss_fn = gs.MSELoss(), X_train = t, Y_train = Xt,
    learning_rate = 0.1, epochs = 10000, print_cost_every = 2000
)

Xc = nig_net(t)
plot_curves(Xc, Xt)

from assets.shapes import airplane

# Generate target curve points
num_pts = 1000
t = torch.linspace(0, 1, num_pts).reshape(-1, 1)
Xt = airplane(num_pts)

preaux_net = PreAuxNet(layer_count = 2, hidden_dim = 25)
monotonic_net = SmoothMinMaxNet(input_dim = 1, n_groups = 10, nodes_per_group = 10)
nig_net = NIGnet(layer_count = 5, preaux_net = preaux_net, monotonic_net = monotonic_net)

automate_training(
    model = nig_net, loss_fn = gs.MSELoss(), X_train = t, Y_train = Xt,
    learning_rate = 0.1, epochs = 10000, print_cost_every = 2000
)

Xc = nig_net(t)
plot_curves(Xc, Xt)