def forward(self, query_points, support_points, neighbors_indices,
features):
"""
This module performs a Kernel Point Convolution. (both normal and strided version)
:param query_points: float32[n_points, dim] - input query points (center of neighborhoods)
:param support_points: float32[n0_points, dim] - input support points (from which neighbors are taken)
:param neighbors_indices: int32[n_points, n_neighbors] - indices of neighbors of each point
:param features: float32[n_points, in_fdim] - input features
:return: output_features float32[n_points, out_fdim]
"""
if self.deformable_v2:
x = conv_ops.KPConv_deformable_v2(
query_points,
support_points,
neighbors_indices,
features,
K_values=self.weight,
w0=self.offset_weight,
b0=self.offset_bias,
fixed=self.config.fixed_kernel_points,
KP_extent=self.config.KP_extent * self.radius /
self.config.density_parameter,
KP_influence=self.config.KP_influence,
aggregation_mode=self.config.convolution_mode,
modulated=self.config.modulated)
else:
x = conv_ops.KPConv_deformable(
query_points,
support_points,
neighbors_indices,
features,
K_values=self.weight,
w0=self.offset_weight,
b0=self.offset_bias,
fixed=self.config.fixed_kernel_points,
KP_extent=self.config.KP_extent * self.radius /
self.config.density_parameter,
KP_influence=self.config.KP_influence,
aggregation_mode=self.config.convolution_mode,
modulated=self.config.modulated)
if self.config.use_batch_norm:
x = self.relu(self.bn(x))
else:
x = self.relu(x)
return x
def KPConv_deformable_v2(query_points, support_points, neighbors_indices, features, K_values, radius, config):
"""
Perform a simple convolution followed by a batch normalization (or a simple bias) and ReLu
"""
# Get KP extent from current radius and config density
extent = config.KP_extent * radius / config.density_parameter
# Convolution
return conv_ops.KPConv_deformable_v2(query_points,
support_points,
neighbors_indices,
features,
K_values,
config.num_kernel_points,
fixed=config.fixed_kernel_points,
KP_extent=extent,
KP_influence=config.KP_influence,
mode=config.convolution_mode,
modulated=config.modulated)