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):
super(BoxEstimationPointDan, self).__init__()
self.num_classes = num_classes
self.g = PointnetG(num_classes)
channels_point = self.g.channels_point
self.attention_s = CALayer(64 * 64)
self.attention_t = CALayer(64 * 64)
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=3, width_multiplier=1):
super().__init__()
self.in_channels = 3
self.num_classes = num_classes
layers, channels = create_mlp_components(
in_channels=self.in_channels,
out_channels=self.blocks,
classifier=False,
dim=2,
width_multiplier=width_multiplier)
self.g = nn.Sequential(*layers)
layers, _ = create_mlp_components(in_channels=(channels + num_classes),
out_channels=[256, 128, 3],
classifier=True,
dim=1,
width_multiplier=width_multiplier)
self.r1 = nn.Sequential(*layers)
layers, _ = create_mlp_components(in_channels=(channels + num_classes),
out_channels=[256, 128, 3],
classifier=True,
dim=1,
width_multiplier=width_multiplier)
self.r2 = 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=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, extra_feature_channels, channels_sa_features, \
sa_in_channels, width_multiplier=1, voxel_resolution_multiplier=1):
super().__init__()
self.in_channels = extra_feature_channels + 3
# only use extra features in the last fp module
sa_in_channels[0] = extra_feature_channels
fp_layers, channels_fp_features = create_pointnet2_fp_modules(
fp_blocks=self.fp_blocks,
in_channels=channels_sa_features,
sa_in_channels=sa_in_channels,
with_se=False,
width_multiplier=width_multiplier,
voxel_resolution_multiplier=voxel_resolution_multiplier)
self.fp_layers = nn.ModuleList(fp_layers)
layers, _ = create_mlp_components(in_channels=channels_fp_features,
out_channels=[64, 0.5, num_classes],
classifier=True,
dim=2,
width_multiplier=width_multiplier)
self.classifier = nn.Sequential(*layers)
layers, _ = create_mlp_components(in_channels=channels_fp_features,
out_channels=[64, 0.5, 32, 3],
classifier=True,
dim=2,
width_multiplier=width_multiplier)
self.reconstructor = 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, sa_blocks, fp_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
sa_layers, sa_in_channels, channels_sa_features, _ = create_pointnet2_sa_components(
sa_blocks=sa_blocks, extra_feature_channels=extra_feature_channels, with_se=False,
width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
)
self.sa_layers = nn.ModuleList(sa_layers)
# use one hot vector in the first fp module
sa_in_channels[-1] += num_classes
fp_layers, channels_fp_features = create_pointnet2_fp_modules(
fp_blocks=fp_blocks, in_channels=channels_sa_features, sa_in_channels=sa_in_channels, with_se=False,
width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
)
self.fp_layers = nn.ModuleList(fp_layers)
layers, _ = create_mlp_components(
in_channels=channels_fp_features, out_channels=[128, 0.3, 2],
classifier=True, dim=2, width_multiplier=width_multiplier
)
self.classifier = nn.Sequential(*layers)
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,
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,
sa_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
sa_layers, _, channels_sa_features, num_centers = create_pointnet2_sa_components(
sa_blocks=sa_blocks,
extra_feature_channels=0,
with_se=False,
width_multiplier=width_multiplier,
voxel_resolution_multiplier=voxel_resolution_multiplier)
self.features = nn.Sequential(*sa_layers)
layers, _ = create_mlp_components(
in_channels=(channels_sa_features * num_centers + 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, num_shapes, sa_blocks, fp_blocks, with_one_hot_shape_id=True,
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
self.with_one_hot_shape_id = with_one_hot_shape_id
sa_layers, sa_in_channels, channels_sa_features, _ = create_pointnet2_sa_components(
sa_blocks=sa_blocks, extra_feature_channels=extra_feature_channels, width_multiplier=width_multiplier
)
self.sa_layers = nn.ModuleList(sa_layers)
# use one hot vector in the last fp module
sa_in_channels[0] += num_shapes if with_one_hot_shape_id else 0
fp_layers, channels_fp_features = create_pointnet2_fp_modules(
fp_blocks=fp_blocks, in_channels=channels_sa_features, sa_in_channels=sa_in_channels,
width_multiplier=width_multiplier, voxel_resolution_multiplier=voxel_resolution_multiplier
)
self.fp_layers = nn.ModuleList(fp_layers)
layers, _ = create_mlp_components(in_channels=channels_fp_features, out_channels=[128, 0.5, num_classes],
classifier=True, dim=2, width_multiplier=width_multiplier)
self.classifier = nn.Sequential(*layers)
def __init__(self, num_classes=3, width_multiplier=1):
super().__init__()
self.g = CenterRegressionPointDanGenerator(num_classes,
width_multiplier)
channels = self.g.channels
self.attention_s = CALayer(64 * 64)
self.attention_t = CALayer(64 * 64)
layers, _ = create_mlp_components(in_channels=(channels + num_classes),
out_channels=[256, 128, 3],
classifier=True,
dim=1,
width_multiplier=width_multiplier)
self.r1 = nn.Sequential(*layers)
layers, _ = create_mlp_components(in_channels=(channels + num_classes),
out_channels=[256, 128, 3],
classifier=True,
dim=1,
width_multiplier=width_multiplier)
self.r2 = nn.Sequential(*layers)
def __init__(self, num_classes, width_multiplier):
super().__init__()
self.in_channels = 3
self.num_classes = num_classes
layers, channels = create_mlp_components(
in_channels=self.in_channels,
out_channels=self.blocks1,
classifier=False,
dim=2,
width_multiplier=width_multiplier)
self.pre = nn.Sequential(*layers)
self.node = adapt_layer_off()
layers, channels = create_mlp_components(
in_channels=2 * channels,
out_channels=self.blocks2,
classifier=False,
dim=2,
width_multiplier=width_multiplier)
self.features = nn.Sequential(*layers)
self.channels = channels
def __init__(self,
num_classes,
extra_feature_channels=7,
width_multiplier=1,
voxel_resolution_multiplier=1
): #extra channel features ändrat 19/2
super().__init__()
self.in_channels = extra_feature_channels + 3
sa_layers, sa_in_channels, channels_sa_features, _ = create_pointnet2_sa_components(
sa_blocks=self.sa_blocks,
extra_feature_channels=extra_feature_channels,
with_se=True,
width_multiplier=width_multiplier,
voxel_resolution_multiplier=voxel_resolution_multiplier,
eps=1e-17)
self.sa_layers = nn.ModuleList(sa_layers)
# only use extra features in the last fp module
sa_in_channels[0] = extra_feature_channels
fp_layers, channels_fp_features = create_pointnet2_fp_modules(
fp_blocks=self.fp_blocks,
in_channels=channels_sa_features,
sa_in_channels=sa_in_channels,
with_se=True,
width_multiplier=width_multiplier,
voxel_resolution_multiplier=voxel_resolution_multiplier,
eps=1e-17)
self.fp_layers = nn.ModuleList(fp_layers)
layers, _ = create_mlp_components(in_channels=channels_fp_features,
out_channels=[128, 0.5, num_classes],
classifier=True,
dim=2,
width_multiplier=width_multiplier)
self.classifier = nn.Sequential(*layers)