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from torch import nn, optim
import math
import torch
import torch.nn.functional as F
from einops import rearrange
import numpy as np
from datetime import datetime
import positional_encoding as PE
"""
FCNet
"""
class ResLayer(nn.Module):
def __init__(self, linear_size):
super(ResLayer, self).__init__()
self.l_size = linear_size
self.nonlin1 = nn.ReLU(inplace=True)
self.nonlin2 = nn.ReLU(inplace=True)
self.dropout1 = nn.Dropout()
self.w1 = nn.Linear(self.l_size, self.l_size)
self.w2 = nn.Linear(self.l_size, self.l_size)
def forward(self, x):
y = self.w1(x)
y = self.nonlin1(y)
y = self.dropout1(y)
y = self.w2(y)
y = self.nonlin2(y)
out = x + y
return out
class FCNet(nn.Module):
def __init__(self, num_inputs, num_classes, dim_hidden):
super(FCNet, self).__init__()
self.inc_bias = False
self.class_emb = nn.Linear(dim_hidden, num_classes, bias=self.inc_bias)
self.feats = nn.Sequential(nn.Linear(num_inputs, dim_hidden),
nn.ReLU(inplace=True),
ResLayer(dim_hidden),
ResLayer(dim_hidden),
ResLayer(dim_hidden),
ResLayer(dim_hidden))
def forward(self, x):
loc_emb = self.feats(x)
class_pred = self.class_emb(loc_emb)
return class_pred
"""A simple Multi Layer Perceptron"""
class MLP(nn.Module):
def __init__(self, input_dim, dim_hidden, num_layers, out_dims):
super(MLP, self).__init__()
layers = []
layers += [nn.Linear(input_dim, dim_hidden, bias=True), nn.ReLU()] # input layer
layers += [nn.Linear(dim_hidden, dim_hidden, bias=True), nn.ReLU()] * num_layers # hidden layers
layers += [nn.Linear(dim_hidden, out_dims, bias=True)] # output layer
self.features = nn.Sequential(*layers)
def forward(self, x):
return self.features(x)
def exists(val):
return val is not None
def cast_tuple(val, repeat = 1):
return val if isinstance(val, tuple) else ((val,) * repeat)
"""Sinusoidal Representation Network (SIREN)"""
class SirenNet(nn.Module):
def __init__(self, dim_in, dim_hidden, dim_out, num_layers, w0 = 1., w0_initial = 30., use_bias = True, final_activation = None, degreeinput = False, dropout = True):
super().__init__()
self.num_layers = num_layers
self.dim_hidden = dim_hidden
self.degreeinput = degreeinput
self.layers = nn.ModuleList([])
for ind in range(num_layers):
is_first = ind == 0
layer_w0 = w0_initial if is_first else w0
layer_dim_in = dim_in if is_first else dim_hidden
self.layers.append(Siren(
dim_in = layer_dim_in,
dim_out = dim_hidden,
w0 = layer_w0,
use_bias = use_bias,
is_first = is_first,
dropout = dropout
))
final_activation = nn.Identity() if not exists(final_activation) else final_activation
self.last_layer = Siren(dim_in = dim_hidden, dim_out = dim_out, w0 = w0, use_bias = use_bias, activation = final_activation, dropout = False)
def forward(self, x, mods = None):
# do some normalization to bring degrees in a -pi to pi range
if self.degreeinput:
x = torch.deg2rad(x) - torch.pi
mods = cast_tuple(mods, self.num_layers)
for layer, mod in zip(self.layers, mods):
x = layer(x)
if exists(mod):
x *= rearrange(mod, 'd -> () d')
return self.last_layer(x)
class Sine(nn.Module):
def __init__(self, w0 = 1.):
super().__init__()
self.w0 = w0
def forward(self, x):
return torch.sin(self.w0 * x)
class Siren(nn.Module):
def __init__(self, dim_in, dim_out, w0 = 1., c = 6., is_first = False, use_bias = True, activation = None, dropout = False):
super().__init__()
self.dim_in = dim_in
self.is_first = is_first
self.dim_out = dim_out
self.dropout = dropout
weight = torch.zeros(dim_out, dim_in)
bias = torch.zeros(dim_out) if use_bias else None
self.init_(weight, bias, c = c, w0 = w0)
self.weight = nn.Parameter(weight)
self.bias = nn.Parameter(bias) if use_bias else None
self.activation = Sine(w0) if activation is None else activation
def init_(self, weight, bias, c, w0):
dim = self.dim_in
w_std = (1 / dim) if self.is_first else (math.sqrt(c / dim) / w0)
weight.uniform_(-w_std, w_std)
if exists(bias):
bias.uniform_(-w_std, w_std)
def forward(self, x):
out = F.linear(x, self.weight, self.bias)
if self.dropout:
out = F.dropout(out, training=self.training)
out = self.activation(out)
return out
class Modulator(nn.Module):
def __init__(self, dim_in, dim_hidden, num_layers):
super().__init__()
self.layers = nn.ModuleList([])
for ind in range(num_layers):
is_first = ind == 0
dim = dim_in if is_first else (dim_hidden + dim_in)
self.layers.append(nn.Sequential(
nn.Linear(dim, dim_hidden),
nn.ReLU()
))
def forward(self, z):
x = z
hiddens = []
for layer in self.layers:
x = layer(x)
hiddens.append(x)
x = torch.cat((x, z))
return tuple(hiddens)
class SirenWrapper(nn.Module):
def __init__(self, net, image_width, image_height, latent_dim = None):
super().__init__()
assert isinstance(net, SirenNet), 'SirenWrapper must receive a Siren network'
self.net = net
self.image_width = image_width
self.image_height = image_height
self.modulator = None
if exists(latent_dim):
self.modulator = Modulator(
dim_in = latent_dim,
dim_hidden = net.dim_hidden,
num_layers = net.num_layers
)
tensors = [torch.linspace(-1, 1, steps = image_height), torch.linspace(-1, 1, steps = image_width)]
mgrid = torch.stack(torch.meshgrid(*tensors, indexing = 'ij'), dim=-1)
mgrid = rearrange(mgrid, 'h w c -> (h w) c')
self.register_buffer('grid', mgrid)
def forward(self, img = None, *, latent = None):
modulate = exists(self.modulator)
assert not (modulate ^ exists(latent)), 'latent vector must be only supplied if `latent_dim` was passed in on instantiation'
mods = self.modulator(latent) if modulate else None
coords = self.grid.clone().detach().requires_grad_()
out = self.net(coords, mods)
out = rearrange(out, '(h w) c -> () c h w', h = self.image_height, w = self.image_width)
if exists(img):
return F.mse_loss(img, out)
return out
def get_positional_encoding(name, legendre_polys=10, harmonics_calculation='analytic', min_radius=1, max_radius=360, frequency_num=10):
if name == "direct":
return PE.Direct()
elif name == "cartesian3d":
return PE.Cartesian3D()
elif name == "sphericalharmonics":
if harmonics_calculation == 'discretized':
return PE.DiscretizedSphericalHarmonics(legendre_polys=legendre_polys)
else:
return PE.SphericalHarmonics(legendre_polys=legendre_polys,
harmonics_calculation=harmonics_calculation)
elif name == "theory":
return PE.Theory(min_radius=min_radius,
max_radius=max_radius,
frequency_num=frequency_num)
elif name == "wrap":
return PE.Wrap()
elif name in ["grid", "spherec", "spherecplus", "spherem", "spheremplus"]:
return PE.GridAndSphere(min_radius=min_radius,
max_radius=max_radius,
frequency_num=frequency_num,
name=name)
else:
raise ValueError(f"{name} not a known positional encoding.")
def get_neural_network(name, input_dim, num_classes=256, dim_hidden=256, num_layers=2):
if name == "linear":
return nn.Linear(input_dim, num_classes)
elif name == "mlp":
return MLP(
input_dim=input_dim,
dim_hidden=dim_hidden,
num_layers=num_layers,
out_dims=num_classes
)
elif name == "siren":
return SirenNet(
dim_in=input_dim,
dim_hidden=dim_hidden,
num_layers=num_layers,
dim_out=num_classes
)
elif name == "fcnet":
return FCNet(
num_inputs=input_dim,
num_classes=num_classes,
dim_hidden=dim_hidden
)
else:
raise ValueError(f"{name} not a known neural networks.")
class LocationEncoder(nn.Module):
def __init__(self, posenc, nnet):
super().__init__()
self.posenc = posenc
self.nnet = nnet
def forward(self, x):
x = self.posenc(x)
return self.nnet(x) |