def __init__(self):
super(Model, self).__init__()
self.conv1 = utils.GraphConv1x1(3, 64, batch_norm=None)
for i in range(5):
module = utils.LapResNet2(64)
self.add_module("rn{}".format(i), module)
self.bn_conv2 = utils.GraphConv1x1(64, 64, batch_norm="pre")
self.fc1 = nn.Linear(64, 10)
def __init__(self):
super(Model, self).__init__()
self.conv1 = utils.GraphConv1x1(6, 128, batch_norm=None)
for i in range(15):
if i % 2 == 0:
module = utils.LapResNet2(128)
else:
module = utils.AvgResNet2(128)
self.add_module("rn{}".format(i), module)
self.conv2 = utils.GraphConv1x1(128, 120, batch_norm="pre")
def __init__(self, layer):
super().__init__()
self.conv1 = utils.GraphConv1x1(3, 128, batch_norm=None)
self.layer = layer
for i in range(self.layer):
if i % 2 == 0:
module = utils.LapResNet2(128)
else:
module = utils.AvgResNet2(128)
self.add_module("rn{}".format(i), module)
self.conv2 = utils.GraphConv1x1(128, 120, batch_norm="pre")
def __init__(self):
super(LapEncoder, self).__init__()
self.conv1 = utils.GraphConv1x1(3, 128, batch_norm=None)
self.num_layers = 5
for i in range(self.num_layers):
module = utils.LapResNet2(128)
self.add_module("rn{}".format(i), module)
self.bn_conv2 = utils.GraphConv1x1(128, 128, batch_norm="pre")
self.fc_mu = nn.Linear(128, 100)
self.fc_logvar = nn.Linear(128, 100)
def __init__(self):
super(LapDecoder, self).__init__()
self.conv_inputs = utils.GraphConv1x1(3, 128, batch_norm=None)
self.conv_noise = utils.GraphConv1x1(100, 128, batch_norm=None)
self.num_layers = 5
for i in range(self.num_layers):
module = utils.LapResNet2(128)
self.add_module("rn{}".format(i), module)
self.bn_conv2 = utils.GraphConv1x1(128, 128, batch_norm="pre")
self.fc_mu = utils.GraphConv1x1(128, 3, batch_norm=None)
self.fc_logvar = nn.Parameter(torch.zeros(1, 1, 1))