def call(self,
points,
queries,
radii,
points_row_splits=None,
queries_row_splits=None):
"""This function computes the neighbors within a radius for each query point.
Arguments:
points: The 3D positions of the input points. *This argument must be
given as a positional argument!*
queries: The 3D positions of the query points.
radii: A radius for each query point.
points_row_splits: Optional 1D vector with the row splits information
if points is batched. This vector is [0, num_points] if there is
only 1 batch item.
queries_row_splits: Optional 1D vector with the row splits information
if queries is batched. This vector is [0, num_queries] if there is
only 1 batch item.
Returns:
3 Tensors in the following order
neighbors_index
The compact list of indices of the neighbors. The corresponding query point
can be inferred from the 'neighbor_count_row_splits' vector.
neighbors_row_splits
The exclusive prefix sum of the neighbor count for the query points including
the total neighbor count as the last element. The size of this array is the
number of queries + 1.
neighbors_distance
Stores the distance to each neighbor if 'return_distances' is True.
Note that the distances are squared if metric is L2.
This is a zero length Tensor if 'return_distances' is False.
"""
if points_row_splits is None:
points_row_splits = tf.cast(tf.stack([0, tf.shape(points)[0]]),
dtype=tf.int64)
if queries_row_splits is None:
queries_row_splits = tf.cast(tf.stack([0, tf.shape(queries)[0]]),
dtype=tf.int64)
result = ops.radius_search(
ignore_query_point=self.ignore_query_point,
return_distances=self.return_distances,
normalize_distances=self.normalize_distances,
metric=self.metric,
points=points,
queries=queries,
radii=radii,
points_row_splits=points_row_splits,
queries_row_splits=queries_row_splits)
return result
def call(self,
inp_features,
inp_positions,
out_positions,
extents,
inp_importance=None,
fixed_radius_search_hash_table=None,
user_neighbors_index=None,
user_neighbors_row_splits=None,
user_neighbors_importance=None):
"""This function computes the output features.
Arguments:
inp_features: A 2D tensor which stores a feature vector for each input
point.
inp_positions: A 2D tensor with the 3D point positions of each input
point. The coordinates for each point is a vector with format [x,y,z].
out_positions: A 2D tensor with the 3D point positions of each output
point. The coordinates for each point is a vector with format [x,y,z].
extents: The extent defines the spatial size of the filter for each
output point.
For 'ball to cube' coordinate mappings the extent defines the
bounding box of the ball.
The shape of the tensor is either [1] or [num output points].
inp_importance: Optional scalar importance value for each input point.
fixed_radius_search_hash_table: A precomputed hash table generated with
build_spatial_hash_table().
This input can be used to explicitly force the reuse of a hash table in
special cases and is usually not needed.
Note that the hash table must have been generated with the same 'points'
array. Note that this parameter is only used if 'extents' is a scalar.
user_neighbors_index: This parameter together with 'user_neighbors_row_splits'
and 'user_neighbors_importance' allows to override the automatic neighbor
search. This is the list of neighbor indices for each output point.
This is a nested list for which the start and end of each sublist is
defined by 'user_neighbors_row_splits'.
user_neighbors_row_splits: Defines the start and end of each neighbors
list in 'user_neighbors_index'.
user_neighbors_importance: Defines a scalar importance value for each
element in 'user_neighbors_index'.
Returns: A tensor of shape [num output points, filters] with the output
features.
"""
offset = self.offset
if inp_importance is None:
inp_importance = tf.ones((0,), dtype=tf.float32)
extents = tf.convert_to_tensor(extents)
return_distances = not self.window_function is None
if not user_neighbors_index is None and not user_neighbors_row_splits is None:
if user_neighbors_importance is None:
neighbors_importance = tf.ones((0,), dtype=tf.float32)
else:
neighbors_importance = user_neighbors_importance
neighbors_index = user_neighbors_index
neighbors_row_splits = user_neighbors_row_splits
else:
if extents.shape.rank == 0:
radius = 0.5 * extents
self.nns = self.fixed_radius_search(
inp_positions,
queries=out_positions,
radius=radius,
hash_table=fixed_radius_search_hash_table)
if return_distances:
if self.radius_search_metric == 'L2':
neighbors_distance_normalized = self.nns.neighbors_distance / (
radius * radius)
else: # L1
neighbors_distance_normalized = self.nns.neighbors_distance / radius
elif extents.shape.rank == 1:
radii = 0.5 * extents
self.nns = ops.radius_search(
ignore_query_point=self.radius_search_ignore_query_points,
return_distances=return_distances,
normalize_distances=return_distances,
metric=self.radius_search_metric,
points=inp_positions,
queries=out_positions,
radii=radii)
else:
raise Exception("extents rank must be 0 or 1")
if self.window_function is None:
neighbors_importance = tf.ones((0,), dtype=tf.float32)
else:
neighbors_importance = self.window_function(
neighbors_distance_normalized)
neighbors_index = self.nns.neighbors_index
neighbors_row_splits = self.nns.neighbors_row_splits
# for stats and debugging
num_pairs = tf.shape(neighbors_index)[0]
self._avg_neighbors = tf.dtypes.cast(
num_pairs, tf.float32) / tf.dtypes.cast(
tf.shape(out_positions)[0], tf.float32)
extents_rank2 = extents
while extents_rank2.shape.rank < 2:
extents_rank2 = tf.expand_dims(extents_rank2, axis=-1)
self._conv_values = {
'filters': self.kernel,
'out_positions': out_positions,
'extents': extents_rank2,
'offset': offset,
'inp_positions': inp_positions,
'inp_features': inp_features,
'inp_importance': inp_importance,
'neighbors_index': neighbors_index,
'neighbors_row_splits': neighbors_row_splits,
'neighbors_importance': neighbors_importance,
'align_corners': self.align_corners,
'coordinate_mapping': self.coordinate_mapping,
'interpolation': self.interpolation,
'normalize': self.normalize,
}
out_features = ops.continuous_conv(**self._conv_values)
self._conv_output = out_features
if self.use_dense_layer_for_center:
self._dense_output = self.dense(inp_features)
out_features = out_features + self._dense_output
if self.use_bias:
out_features += self.bias
out_features = self.activation(out_features)
return out_features