def __init__(self, num_classes, normal_channel=False):
super(get_model, self).__init__()
if normal_channel:
additional_channel = 3
else:
additional_channel = 0
self.normal_channel = normal_channel
self.sa1 = PointNetSetAbstractionMsg(
512, [0.1, 0.2, 0.4], [32, 64, 128], 3 + additional_channel,
[[32, 32, 64], [64, 64, 128], [64, 96, 128]])
self.sa2 = PointNetSetAbstractionMsg(
128, [0.4, 0.8], [64, 128], 128 + 128 + 64,
[[128, 128, 256], [128, 196, 256]])
self.sa3 = PointNetSetAbstraction(npoint=None,
radius=None,
nsample=None,
in_channel=512 + 3,
mlp=[256, 512, 1024],
group_all=True)
self.fp3 = PointNetFeaturePropagation(in_channel=1536, mlp=[256, 256])
self.fp2 = PointNetFeaturePropagation(in_channel=576, mlp=[256, 128])
self.fp1 = PointNetFeaturePropagation(in_channel=150 +
additional_channel,
mlp=[128, 128])
self.conv1 = nn.Conv1d(128, 128, 1)
self.bn1 = nn.BatchNorm1d(128)
self.drop1 = nn.Dropout(0.5)
self.conv2 = nn.Conv1d(128, num_classes, 1)
def __init__(self, num_classes):
super(get_model, self).__init__()
self.sa1 = PointNetSetAttention(1024, 0.1, 32, 9 + 3, [32, 32, 64], False)
self.sa2 = PointNetSetAttention(256, 0.2, 32, 64 + 3, [64, 64, 128], False)
self.sa3 = PointNetSetAttention(64, 0.4, 32, 128 + 3, [128, 128, 256], False)
self.sa4 = PointNetSetAttention(16, 0.8, 32, 256 + 3, [256, 256, 512], False)
self.fp4 = PointNetFeaturePropagation(768, [256, 256])
self.fp3 = PointNetFeaturePropagation(384, [256, 256])
self.fp2 = PointNetFeaturePropagation(320, [256, 128])
self.fp1 = PointNetFeaturePropagation(128, [128, 128, 128])
self.conv1 = nn.Conv1d(128, 128, 1)
self.bn1 = nn.BatchNorm1d(128)
self.drop1 = nn.Dropout(0.5)
self.conv2 = nn.Conv1d(128, num_classes, 1)
def __init__(self):
super(pointnet2_backbone, self).__init__()
#self.sa1 = PointNetSetAbstractionMsg(1024, [0.05, 0.1], [16, 32], 9, [[16, 16, 32], [32, 32, 64]])
self.sa1 = PointNetSetAbstractionMsg(1024, [0.05, 0.1], [16, 32], 0, [[16, 16, 32], [32, 32, 64]])
self.sa2 = PointNetSetAbstractionMsg(256, [0.1, 0.2], [16, 32], 32+64, [[64, 64, 128], [64, 96, 128]])
self.sa3 = PointNetSetAbstractionMsg(64, [0.2, 0.4], [16, 32], 128+128, [[128, 196, 256], [128, 196, 256]])
#self.sa4 = PointNetSetAbstractionMsg(16, [0.4, 0.8], [16, 32], 256+256, [[256, 256, 512], [256, 384, 512]])
self.sa4 = PointNetSetAbstraction(None, None, None, 256+256+3, [256, 512, 512], True)
self.fp4 = PointNetFeaturePropagation(512+512, [256, 256])
self.fp3 = PointNetFeaturePropagation(128+128+256, [256, 256])
self.fp2 = PointNetFeaturePropagation(32+64+256, [256, 128])
self.fp1 = PointNetFeaturePropagation(128, [128, 128, 128])
'''
def __init__(self, num_classes, normal_channel=False):
super(get_model, self).__init__()
if normal_channel:
additional_channel = 3
else:
additional_channel = 0
self.normal_channel = normal_channel
self.sa1 = PointNetSetAbstraction(npoint=512, radius=0.2, nsample=32, in_channel=6+additional_channel, mlp=[64, 64, 128], group_all=False)
self.sa2 = PointNetSetAbstraction(npoint=128, radius=0.4, nsample=64, in_channel=128 + 3, mlp=[128, 128, 256], group_all=False)
self.sa3 = PointNetSetAbstraction(npoint=None, radius=None, nsample=None, in_channel=256 + 3, mlp=[256, 512, 1024], group_all=True)
self.fp3 = PointNetFeaturePropagation(in_channel=1280, mlp=[256, 256])
self.fp2 = PointNetFeaturePropagation(in_channel=384, mlp=[256, 128])
self.fp1 = PointNetFeaturePropagation(in_channel=128+16+6+additional_channel, mlp=[128, 128, 128])
self.conv1 = nn.Conv1d(128, 128, 1)
self.bn1 = nn.BatchNorm1d(128)
self.drop1 = nn.Dropout(0.5)
self.conv2 = nn.Conv1d(128, num_classes, 1)
def __init__(self, num_classes):
super(get_model, self).__init__()
self.sa1 = PointNetSetAbstractionMsg(1024, [0.05, 0.1], [16, 32], 9,
[[16, 16, 32], [32, 32, 64]])
self.sa2 = PointNetSetAbstractionMsg(256, [0.1, 0.2], [16, 32],
32 + 64,
[[64, 64, 128], [64, 96, 128]])
self.sa3 = PointNetSetAbstractionMsg(
64, [0.2, 0.4], [16, 32], 128 + 128,
[[128, 196, 256], [128, 196, 256]])
self.sa4 = PointNetSetAbstractionMsg(
16, [0.4, 0.8], [16, 32], 256 + 256,
[[256, 256, 512], [256, 384, 512]])
self.fp4 = PointNetFeaturePropagation(512 + 512 + 256 + 256,
[256, 256])
self.fp3 = PointNetFeaturePropagation(128 + 128 + 256, [256, 256])
self.fp2 = PointNetFeaturePropagation(32 + 64 + 256, [256, 128])
self.fp1 = PointNetFeaturePropagation(128, [128, 128, 128])
self.conv1 = nn.Conv1d(128, 128, 1)
self.bn1 = nn.BatchNorm1d(128)
self.drop1 = nn.Dropout(0.5)
self.conv2 = nn.Conv1d(128, num_classes, 1)
