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siren model added #3

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94 changes: 47 additions & 47 deletions models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,66 +58,66 @@ def forward(self, x):
return torch.cat([torch.sin(x_), torch.cos(x_)], dim=1) # (B, 2F)


# class Siren(nn.Module):
# def __init__(self, in_features, hidden_features, hidden_layers, out_features, outermost_linear=False,
# first_omega_0=30, hidden_omega_0=30.):
# super().__init__()
class Siren(nn.Module):
def __init__(self, in_features=2, out_features=3, hidden_features = 256, hidden_layers =4, outermost_linear=False,
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))
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))
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)
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)
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.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)
self.net = nn.Sequential(*self.net)

# def forward(self, x):
# return self.net(x)
def forward(self, x):
return self.net(x)

# class SineLayer(nn.Module):
# # See paper sec. 3.2, final paragraph, and supplement Sec. 1.5 for discussion of omega_0.
class SineLayer(nn.Module):
# See paper sec. 3.2, final paragraph, and supplement Sec. 1.5 for discussion of omega_0.

# # If is_first=True, omega_0 is a frequency factor which simply multiplies the activations before the
# # nonlinearity. Different signals may require different omega_0 in the first layer - this is a
# # hyperparameter.
# If is_first=True, omega_0 is a frequency factor which simply multiplies the activations before the
# nonlinearity. Different signals may require different omega_0 in the first layer - this is a
# hyperparameter.

# # If is_first=False, then the weights will be divided by omega_0 so as to keep the magnitude of
# # activations constant, but boost gradients to the weight matrix (see supplement Sec. 1.5)
# If is_first=False, then the weights will be divided by omega_0 so as to keep the magnitude of
# activations constant, but boost gradients to the weight matrix (see supplement Sec. 1.5)

# 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
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.in_features = in_features
self.linear = nn.Linear(in_features, out_features, bias=bias)

# self.init_weights()
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 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, x):
# return torch.sin(self.omega_0 * self.linear(x))
def forward(self, x):
return torch.sin(self.omega_0 * self.linear(x))

3 changes: 3 additions & 0 deletions opt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,9 @@ def get_opts():
# gaussian embedding scale factor
parser.add_argument('--sc', type=float, default=10., help='Gaussian embedding scale factor')

# omega for siren
parser.add_argument('--omega', type=float, default=30., help='Omega for siren')

# batch size with 4
parser.add_argument('--batch_size', type=int, default=1024, help='Batch size')

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9 changes: 8 additions & 1 deletion train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
from dataset import ImageDataset

#models
from models import MLP, PE
from models import MLP, PE, Siren

#optimizer
from torch.optim import Adam
Expand Down Expand Up @@ -45,11 +45,18 @@ def __init__(self, hparams):
self.pe = PE(P)
self.net = MLP(n_input=self.pe.out_dim)

elif hparams.arch == 'siren':
self.net = Siren( hidden_omega_0= hparams.omega,
first_omega_0= hparams.omega,
)

self.loss = MSELoss()

def forward(self, x):
if hparams.arch == 'identity':
return self.net(x)
elif hparams.arch == 'siren':
return self.net(x)
else:
return self.net(self.pe(x))

Expand Down