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import torch
import torch.nn as nn
import numpy as np
from model.siren import SIREN
class PosNetwork(nn.Module):
def __init__(self,
input_dim,
output_dim,
hidden_dim,
n_layers,
skip_in=(4,),
weight_norm=True):
super(PosNetwork, self).__init__()
dims = [input_dim] + [hidden_dim for _ in range(n_layers)] + [output_dim]
self.num_layers = n_layers
self.skip_in = skip_in
self.siren_layers = nn.ModuleList()
for l in range(0, self.num_layers + 1):
if l + 1 in self.skip_in:
out_dim = dims[l + 1] + dims[0]
dims[l + 1] = out_dim
else:
out_dim = dims[l + 1]
lin = nn.Linear(dims[l], out_dim)
if weight_norm:
lin = nn.utils.weight_norm(lin)
setattr(self, "lin" + str(l), lin)
self.activation = nn.ReLU
def forward(self, inputs):
x = inputs
for l in range(0, self.num_layers + 1):
lin = getattr(self, "lin" + str(l))
if l + 1 in self.skip_in:
x = torch.cat([x, inputs], 1) / np.sqrt(2)
x = lin(x)
if l < self.num_layers:
x = self.activation()(x)
x = x.mean(dim=1) # Pooling to match Shape: (32, 128)
return x |