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SemanticKitti inf while loop correction

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HuguesTHOMAS 2021-07-29 21:26:45 +00:00
parent 7fdbc57f9b
commit 7f5f52b067
1 changed files with 21 additions and 19 deletions
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40
datasets/SemanticKitti.py
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@ -762,27 +762,29 @@ class SemanticKittiSampler(Sampler):
# Get the potentials of the frames containing this class
class_potentials = self.dataset.potentials[self.dataset.class_frames[i]]
# Get the indices to generate thanks to potentials
used_classes = self.dataset.num_classes - len(self.dataset.ignored_labels)
class_n = num_centers // used_classes + 1
if class_n < class_potentials.shape[0]:
_, class_indices = torch.topk(class_potentials, class_n, largest=False)
else:
class_indices = torch.zeros((0,), dtype=torch.int32)
while class_indices.shape[0] < class_n:
new_class_inds = torch.randperm(class_potentials.shape[0]).type(torch.int32)
class_indices = torch.cat((class_indices, new_class_inds), dim=0)
class_indices = class_indices[:class_n]
class_indices = self.dataset.class_frames[i][class_indices]
if class_potentials.shape[0] > 0:
# Add the indices to the generated ones
gen_indices.append(class_indices)
gen_classes.append(class_indices * 0 + c)
# Get the indices to generate thanks to potentials
used_classes = self.dataset.num_classes - len(self.dataset.ignored_labels)
class_n = num_centers // used_classes + 1
if class_n < class_potentials.shape[0]:
_, class_indices = torch.topk(class_potentials, class_n, largest=False)
else:
class_indices = torch.zeros((0,), dtype=torch.int32)
while class_indices.shape[0] < class_n:
new_class_inds = torch.randperm(class_potentials.shape[0]).type(torch.int32)
class_indices = torch.cat((class_indices, new_class_inds), dim=0)
class_indices = class_indices[:class_n]
class_indices = self.dataset.class_frames[i][class_indices]
# Update potentials
update_inds = torch.unique(class_indices)
self.dataset.potentials[update_inds] = torch.ceil(self.dataset.potentials[update_inds])
self.dataset.potentials[update_inds] += torch.from_numpy(np.random.rand(update_inds.shape[0]) * 0.1 + 0.1)
# Add the indices to the generated ones
gen_indices.append(class_indices)
gen_classes.append(class_indices * 0 + c)
# Update potentials
update_inds = torch.unique(class_indices)
self.dataset.potentials[update_inds] = torch.ceil(self.dataset.potentials[update_inds])
self.dataset.potentials[update_inds] += torch.from_numpy(np.random.rand(update_inds.shape[0]) * 0.1 + 0.1)
# Stack the chosen indices of all classes
gen_indices = torch.cat(gen_indices, dim=0)