def build_optimizer(self):
"""
Create optimizer
"""
self.optimizer = optim.Adam(self.network.parameters(), lr=self.opt.lrate)
if self.opt.reload:
self.optimizer.load_state_dict(torch.load(f'{self.opt.checkpointname}'))
my_utils.yellow_print("Reloaded optimizer")
def __init__(self,
num_points=6890,
encoder_type="Pointnet",
bottleneck_size=1024,
nb_primitives=1,
hidden_sizes=[64, 64, 64, 64, 64],
resnet_layers=True,
skip_connections=False):
self.hidden_sizes = hidden_sizes
self.resnet_layers = resnet_layers
super(AE_Meta_AtlasNet, self).__init__()
self.num_points = num_points
self.bottleneck_size = bottleneck_size
self.nb_primitives = nb_primitives
self.encoder_type = encoder_type
print("Using encoder type : " + self.encoder_type)
self.point_encoder_1 = PointNetfeat(num_points,
global_feat=True,
trans=False)
self.point_encoder_2 = PointNetfeat(num_points,
global_feat=True,
trans=False)
self.encoder = nn.Sequential(self.point_encoder_1,
nn.Linear(1024, 512), nn.BatchNorm1d(512),
nn.ReLU())
self.encoder2 = nn.Sequential(self.point_encoder_2,
nn.Linear(1024,
512), nn.BatchNorm1d(512),
nn.ReLU())
self.decoder = MetaPointGenCon2(bottleneck_size=self.bottleneck_size,
hidden_sizes=hidden_sizes,
resnet_layers=resnet_layers)
self.skip_connections = skip_connections
if self.skip_connections:
my_utils.yellow_print("Enable Skip_connections in pointcloud MLP")
self.forward = self.forward_resnet
else:
my_utils.yellow_print("Desable Skip_connections in pointcloud MLP")
self.forward = self.forward_classic
