siren.py

import numpy as np
import torch
import torch.nn as nn

class SineLayer(nn.Module):
    
    def __init__(self, in_features, out_features, bias=True,
                 is_first=False, omega_0=30):
        super().__init__()
        self.omega_0 = omega_0
        self.is_first = is_first
        
        self.in_features = in_features
        self.linear = nn.Linear(in_features, out_features, bias=bias)
        
        self.init_weights()
    
    def init_weights(self):
        with torch.no_grad():
            if self.is_first:
                self.linear.weight.uniform_(-1 / self.in_features, 
                                             1 / self.in_features)      
            else:
                self.linear.weight.uniform_(-np.sqrt(6 / self.in_features) / self.omega_0, 
                                             np.sqrt(6 / self.in_features) / self.omega_0)
        
    def forward(self, input):
        return torch.sin(self.omega_0 * self.linear(input))
    
class Siren(nn.Module):
    def __init__(self, in_features, hidden_features, hidden_layers, out_features, outermost_linear=True, 
                 first_omega_0=30, hidden_omega_0=30.):
        super().__init__()
        
        self.net = []
        self.net.append(SineLayer(in_features, hidden_features, 
                                  is_first=True, omega_0=first_omega_0))

        for i in range(hidden_layers):
            self.net.append(SineLayer(hidden_features, hidden_features, 
                                      is_first=False, omega_0=hidden_omega_0))
        if outermost_linear:
            final_linear = nn.Linear(hidden_features, out_features)
            
            with torch.no_grad():
                final_linear.weight.uniform_(-np.sqrt(6 / hidden_features) / hidden_omega_0, 
                                              np.sqrt(6 / hidden_features) / hidden_omega_0)
                
            self.net.append(final_linear)
        else:
            self.net.append(SineLayer(hidden_features, out_features, 
                                      is_first=False, omega_0=hidden_omega_0))
        
        self.net = nn.Sequential(*self.net).cuda()
        
    def forward(self, coords):
        output = self.net(coords)
        return output
    
    
def gradient(y, x, grad_outputs=None):
    if grad_outputs is None:
        grad_outputs = torch.ones_like(y)
    grad = torch.autograd.grad(y, [x], grad_outputs=grad_outputs, create_graph=True)[0]
    return grad