def __init__(self,
num_classes,
point_blocks,
cloud_blocks,
extra_feature_channels,
width_multiplier=1,
voxel_resolution_multiplier=1):
super().__init__()
self.in_channels = extra_feature_channels + 3
self.num_classes = num_classes
layers, channels_point, _ = create_pointnet_components(
blocks=point_blocks,
in_channels=self.in_channels,
with_se=False,
width_multiplier=width_multiplier,
voxel_resolution_multiplier=voxel_resolution_multiplier)
self.point_features = nn.Sequential(*layers)
layers, channels_cloud, _ = create_pointnet_components(
blocks=cloud_blocks,
in_channels=channels_point,
with_se=False,
width_multiplier=width_multiplier,
voxel_resolution_multiplier=voxel_resolution_multiplier)
self.cloud_features = nn.Sequential(*layers)
layers, _ = create_mlp_components(
in_channels=(channels_point + channels_cloud + num_classes),
out_channels=[512, 256, 128, 128, 0.5, 2],
classifier=True,
dim=2,
width_multiplier=width_multiplier)
self.classifier = nn.Sequential(*layers)
def __init__(self,
num_classes,
num_shapes,
extra_feature_channels=3,
width_multiplier=1,
voxel_resolution_multiplier=1):
super().__init__()
assert extra_feature_channels >= 0
self.in_channels = extra_feature_channels + 3
self.num_shapes = num_shapes
layers, channels_point, concat_channels_point = create_pointnet_components(
blocks=self.blocks,
in_channels=self.in_channels,
with_se=True,
normalize=False,
width_multiplier=width_multiplier,
voxel_resolution_multiplier=voxel_resolution_multiplier)
self.point_features = nn.ModuleList(layers)
layers, _ = create_mlp_components(
in_channels=(num_shapes + channels_point + concat_channels_point),
out_channels=[256, 0.2, 256, 0.2, 128, num_classes],
classifier=True,
dim=2,
width_multiplier=width_multiplier)
self.classifier = nn.Sequential(*layers)
def __init__(self,
num_classes,
extra_feature_channels=6,
width_multiplier=1):
super().__init__()
self.in_channels = extra_feature_channels + 3
layers, channels_point, _ = create_pointnet_components(
blocks=self.blocks,
in_channels=self.in_channels,
width_multiplier=width_multiplier)
self.point_features = nn.Sequential(*layers)
layers, channels_cloud = create_mlp_components(
in_channels=channels_point,
out_channels=[256, 128],
classifier=False,
dim=1,
width_multiplier=width_multiplier)
self.cloud_features = nn.Sequential(*layers)
layers, _ = create_mlp_components(
in_channels=(channels_point + channels_cloud),
out_channels=[512, 256, 0.3, num_classes],
classifier=True,
dim=2,
width_multiplier=width_multiplier)
self.classifier = nn.Sequential(*layers)
def __init__(self, num_classes=3, extra_feature_channels=1,
num_heading_angle_bins=12, num_size_templates=8,
width_multiplier=1, voxel_resolution_multiplier=1):
super(BoxEstimationSimpleDanNet, self).__init__()
self.in_channels = 3
self.num_classes = num_classes
blocks = ((128, 2, None), (256, 1, None), (512, 1, None))
layers, channels_point, _ = create_pointnet_components(
blocks=blocks, in_channels=self.in_channels, with_se=False, normalize=True, eps=1e-15,
width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
)
self.g = nn.Sequential(*layers)
layers, _ = create_mlp_components(
in_channels=channels_point + num_classes,
out_channels=[512, 256, (3 + num_heading_angle_bins * 2 + num_size_templates * 4)],
classifier=True, dim=1, width_multiplier=width_multiplier
)
self.c1 = nn.Sequential(*layers)
layers, _ = create_mlp_components(
in_channels=channels_point + num_classes,
out_channels=[512, 256, (3 + num_heading_angle_bins * 2 + num_size_templates * 4)],
classifier=True, dim=1, width_multiplier=width_multiplier
)
self.c2 = nn.Sequential(*layers)
def __init__(self,
num_classes,
blocks,
num_heading_angle_bins,
num_size_templates,
width_multiplier=1,
voxel_resolution_multiplier=1):
super().__init__()
self.in_channels = 3
self.num_classes = num_classes
layers, channels_point, _ = create_pointnet_components(
blocks=blocks,
in_channels=self.in_channels,
with_se=False,
normalize=True,
eps=1e-15,
width_multiplier=width_multiplier,
voxel_resolution_multiplier=voxel_resolution_multiplier)
self.features = nn.Sequential(*layers)
layers, _ = create_mlp_components(
in_channels=channels_point + num_classes,
out_channels=[
512, 256,
(3 + num_heading_angle_bins * 2 + num_size_templates * 4)
],
classifier=True,
dim=1,
width_multiplier=width_multiplier)
self.classifier = nn.Sequential(*layers)
def __init__(self,
num_classes,
extra_feature_channels=7,
width_multiplier=1,
voxel_resolution_multiplier=2):
super().__init__()
self.in_channels = extra_feature_channels + 3
layers, channels_point, concat_channels_point = create_pointnet_components(
blocks=self.blocks,
in_channels=self.in_channels,
with_se=False,
eps=1e-17,
width_multiplier=width_multiplier,
voxel_resolution_multiplier=voxel_resolution_multiplier)
self.point_features = nn.ModuleList(layers)
layers, channels_cloud = create_mlp_components(
in_channels=channels_point,
out_channels=[256, 128],
classifier=False,
dim=1,
width_multiplier=width_multiplier)
self.cloud_features = nn.Sequential(*layers)
layers, _ = create_mlp_components(
in_channels=(concat_channels_point + channels_cloud),
out_channels=[512, 0.3, 256, 0.3, num_classes],
classifier=True,
dim=2,
width_multiplier=width_multiplier)
self.classifier = nn.Sequential(*layers)
def __init__(self, num_classes, width_multiplier=1, voxel_resolution_multiplier=1):
super(PointnetG, self).__init__()
self.trans_net1 = transform_net(3, 3)
blocks1 = ((64, 3, None),)
blocks2 = ((256, 1, None), (512, 1, None))
self.in_channels = 3
self.num_classes = num_classes
layers, channels_point, _ = create_pointnet_components(
blocks=blocks1, in_channels=self.in_channels, with_se=False, normalize=True, eps=1e-15,
width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
)
self.pre = nn.Sequential(*layers)
self.node = adapt_layer_off(trans_dim_in=channels_point, trans_dim_out=channels_point, fc_dim=channels_point)
layers, channels_point, _ = create_pointnet_components(
blocks=blocks2, in_channels=2*channels_point, with_se=False, normalize=True, eps=1e-15,
width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
)
self.features = nn.Sequential(*layers)
self.channels_point = channels_point
