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import torch
import logging
import src
log = logging.getLogger(__name__)
__all__ = ['SemanticSegmentationOutput']
class SemanticSegmentationOutput:
"""A simple holder for semantic segmentation model output, with a
few helper methods for manipulating the predictions and targets
(if any).
"""
def __init__(self, logits, y_hist=None):
self.logits = logits
self.y_hist = y_hist
if src.is_debug_enabled():
self.debug()
def debug(self):
"""Runs a series of sanity checks on the attributes of self.
"""
assert isinstance(self.logits, torch.Tensor) \
or all(isinstance(l, torch.Tensor) for l in self.logits)
if self.has_target:
if self.multi_stage:
assert len(self.y_hist) == len(self.logits)
assert all(
y.shape[0] == l.shape[0]
for y, l in zip(self.y_hist, self.logits))
else:
assert self.y_hist.shape[0] == self.logits.shape[0]
@property
def device(self):
"""Returns the device on which the logits are stored, assuming
all other output variables held by the object are also on the
same device.
"""
logits = self.logits[0] if self.multi_stage else self.logits
return logits.device
@property
def has_target(self):
"""Check whether `self` contains target data for semantic
segmentation.
"""
return self.y_hist is not None
@property
def multi_stage(self):
"""If the semantic segmentation `logits` are stored in an
enumerable, then the model output is multi-stage.
"""
return not isinstance(self.logits, torch.Tensor)
@property
def num_classes(self):
"""Number for semantic classes in the output predictions.
"""
logits = self.logits[0] if self.multi_stage else self.logits
return logits.shape[1]
@property
def num_nodes(self):
"""Number for nodes/superpoints in the output predictions. By
default, for a hierarchical partition, this means counting the
number of level-1 nodes/superpoints.
"""
logits = self.logits[0] if self.multi_stage else self.logits
return logits.shape[0]
def semantic_pred(self):
"""Semantic predictions on the level-1 superpoint.
Final semantic segmentation predictions are the argmax of the
first-level partition logits.
"""
logits = self.logits[0] if self.multi_stage else self.logits
return torch.argmax(logits, dim=1)
@property
def semantic_target(self):
"""Semantic target on the level-1 superpoint.
Final semantic segmentation target are the label histogram
of the first-level partition logits.
"""
return self.y_hist[0] if self.multi_stage else self.y_hist
@property
def void_mask(self):
"""Returns a mask on the level-1 nodes indicating which is void.
By convention, nodes/superpoints are void if they contain
more than 50% void points. By convention in this project, void
points have the label `num_classes`. In label histograms, void
points are counted in the last column.
"""
if not self.has_target:
return
# For simplicity, we only return the mask for the level-1
y_hist = self.semantic_target
total_count = y_hist.sum(dim=1)
void_count = y_hist[:, -1]
return void_count / total_count > 0.5
def __repr__(self):
return f"{self.__class__.__name__}()"
def voxel_semantic_pred(self, super_index=None, sub=None):
"""Semantic predictions on the level-0 voxels.
Final semantic segmentation predictions are the argmax of the
first-level partition logits. This function then distributes
these predictions to each level-0 point (ie voxel in our
framework).
:param super_index: LongTensor
Tensor holding, for each level-0 point (ie voxel), the index
of the level-1 superpoint it belongs to
:param sub: Cluster
Cluster object indicating, for each level-1 superpoint,
the indices of the level-0 points (ie voxels) it contains
"""
assert super_index is not None or sub is not None, \
"Must provide either `super_index` or `sub`"
# If super_index is not provided, build it from sub
if super_index is None:
super_index = sub.to_super_index()
# Distribute the level-1 superpoint predictions to the voxels
return self.semantic_pred()[super_index]
def voxel_logits_pred(self, super_index=None, sub=None):
"""Semantic predictions on the level-0 voxels.
Final semantic segmentation predictions are the argmax of the
first-level partition logits. This function then distributes
these predictions to each level-0 point (ie voxel in our
framework).
:param super_index: LongTensor
Tensor holding, for each level-0 point (ie voxel), the index
of the level-1 superpoint it belongs to
:param sub: Cluster
Cluster object indicating, for each level-1 superpoint,
the indices of the level-0 points (ie voxels) it contains
"""
assert super_index is not None or sub is not None, \
"Must provide either `super_index` or `sub`"
# If super_index is not provided, build it from sub
if super_index is None:
super_index = sub.to_super_index()
# Distribute the level-1 superpoint logits to the voxels
return self.logits[0][super_index]
def full_res_semantic_pred(
self,
super_index_level0_to_level1=None,
super_index_raw_to_level0=None,
sub_level1_to_level0=None,
sub_level0_to_raw=None):
"""Semantic predictions on the full-resolution input point
cloud.
Final semantic segmentation predictions are the argmax of the
first-level partition logits. This function then distributes
these predictions to each raw point (ie full-resolution point
cloud before voxelization in our framework).
:param super_index_level0_to_level1: LongTensor
Tensor holding, for each level-0 point (ie voxel), the index
of the level-1 superpoint it belongs to
:param super_index_raw_to_level0: LongTensor
Tensor holding, for each raw full-resolution point, the
index of the level-0 point (ie voxel) it belongs to
:param sub_level1_to_level0: Cluster
Cluster object indicating, for each level-1 superpoint,
the indices of the level-0 points (ie voxels) it contains
:param sub_level0_to_raw: Cluster
Cluster object indicating, for each level-0 point (ie
voxel), the indices of the raw full-resolution points it
contains
"""
assert super_index_level0_to_level1 is not None or sub_level1_to_level0 is not None, \
"Must provide either `super_index_level0_to_level1` or `sub_level1_to_level0`"
assert super_index_raw_to_level0 is not None or sub_level0_to_raw is not None, \
"Must provide either `super_index_raw_to_level0` or `sub_level0_to_raw`"
# If super_index are not provided, build them from sub
if super_index_level0_to_level1 is None:
super_index_level0_to_level1 = sub_level1_to_level0.to_super_index()
if super_index_raw_to_level0 is None:
super_index_raw_to_level0 = sub_level0_to_raw.to_super_index()
# Distribute the level-1 superpoint predictions to the
# full-resolution points
return self.semantic_pred()[super_index_level0_to_level1][super_index_raw_to_level0]
def full_res_logits_pred(
self,
super_index_level0_to_level1=None,
super_index_raw_to_level0=None,
sub_level1_to_level0=None,
sub_level0_to_raw=None):
"""Logits on the full-resolution input point cloud.
This function propagates the level-1 superpoint logits to each
raw point (ie full-resolution point cloud before voxelization).
:param super_index_level0_to_level1: LongTensor
For each level-0 point (voxel), the index of the level-1 superpoint it belongs to.
:param super_index_raw_to_level0: LongTensor
For each raw point, the index of the level-0 point it belongs to.
:param sub_level1_to_level0: Cluster
Optional. Used to build `super_index_level0_to_level1` if not given.
:param sub_level0_to_raw: Cluster
Optional. Used to build `super_index_raw_to_level0` if not given.
:return: Tensor of shape (N_raw, C), where N_raw is the number of raw points,
and C is the number of classes.
"""
assert super_index_level0_to_level1 is not None or sub_level1_to_level0 is not None, \
"Must provide either `super_index_level0_to_level1` or `sub_level1_to_level0`"
assert super_index_raw_to_level0 is not None or sub_level0_to_raw is not None, \
"Must provide either `super_index_raw_to_level0` or `sub_level0_to_raw`"
if super_index_level0_to_level1 is None:
super_index_level0_to_level1 = sub_level1_to_level0.to_super_index()
if super_index_raw_to_level0 is None:
super_index_raw_to_level0 = sub_level0_to_raw.to_super_index()
return self.logits[0][super_index_level0_to_level1][super_index_raw_to_level0]
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