def __init__(self, num_point, batch_size, momentum=0.1):
super(PointNet, self).__init__()
self.conv1 = torch_util.conv2d(in_channels=1,
out_channels=64,
kernel_size=(1, 3),
momentum=momentum)
self.conv2 = torch_util.conv2d(in_channels=64,
out_channels=64,
kernel_size=(1, 1),
momentum=momentum)
self.conv3 = torch_util.conv2d(in_channels=64,
out_channels=64,
kernel_size=(1, 1),
momentum=momentum)
self.conv4 = torch_util.conv2d(in_channels=64,
out_channels=128,
kernel_size=(1, 1),
momentum=momentum)
self.conv5 = torch_util.conv2d(in_channels=128,
out_channels=1024,
kernel_size=(1, 1),
momentum=momentum)
self.fc1 = torch_util.fully_connected(1024, 512)
self.fc2 = torch_util.fully_connected(512, 256)
self.fc3 = nn.Linear(256, 10)
self.feature_transform = feature_transform_net(in_channels=64,
momentum=momentum,
num_point=num_point,
batch_size=batch_size)
self.maxpool = nn.MaxPool2d((num_point, 1))
self.relu = nn.ReLU()
self.drop1 = nn.Dropout(p=0.7)
self.drop2 = nn.Dropout(p=0.7)
def __init__(self):
super(SegNet, self).__init__()
self.conv1 = torch_util.conv2d(1, 16, kernel_size=5, padding=2)
self.conv2 = torch_util.conv2d(16, 64, kernel_size=3, padding=1)
self.conv3 = torch_util.conv2d(64, 256, kernel_size=3, padding=1)
self.conv3_drop = nn.Dropout2d()
self.conv4 = torch_util.conv2d(256, 64, kernel_size=1)
self.conv4_drop = nn.Dropout2d()
self.conv5 = torch_util.conv2d(64, 2, kernel_size=1)
def __init__(self):
super(CNN, self).__init__()
self.conv1 = torch_util.conv2d(1, 16, kernel_size=4)
self.conv2 = torch_util.conv2d(16, 64, kernel_size=3)
self.conv3 = torch_util.conv2d(64, 256, kernel_size=3)
self.conv4 = torch_util.conv2d(256, 1024, kernel_size=4)
self.conv3_drop = nn.Dropout2d()
self.fc1 = torch_util.fully_connected(1024 * 3 * 3, 256)
self.fc2 = torch_util.fully_connected(256, 64)
self.fc3 = torch_util.fully_connected(64, 10)
def __init__(self, in_channels, momentum, num_point, batch_size):
super(feature_transform_net, self).__init__()
self.batch_size = batch_size
self.conv1 = torch_util.conv2d(in_channels, 64, (1, 1), momentum)
self.conv2 = torch_util.conv2d(64, 128, (1, 1), momentum)
self.conv3 = torch_util.conv2d(128, 1024, (1, 1), momentum)
self.maxpool = nn.MaxPool2d((num_point, 1))
self.fc1 = torch_util.fully_connected(1024, 512)
self.fc2 = torch_util.fully_connected(512, 256)
self.fc3 = nn.Linear(256, 64 * 64)