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import torch
def convert_gt_to_one_hot(gt_segments, gt_labels, num_classes):
"""convert the gt from class index to one hot encoding. this is for multi class case."""
gt_segments_unique, gt_labels_onehot = [], []
for gt_segment, gt_label in zip(gt_segments, gt_labels):
if len(gt_segment) > 0:
bbox_unique, inverse_indices = torch.unique(gt_segment, dim=0, return_inverse=True)
label_unique = []
for i in range(bbox_unique.shape[0]):
label = torch.nn.functional.one_hot(
gt_label[inverse_indices == i].long(),
num_classes=num_classes,
)
label_unique.append(label.sum(dim=0).to(gt_label.device))
label_unique = torch.stack(label_unique)
else:
bbox_unique, label_unique = [], []
gt_segments_unique.append(bbox_unique) # [K]
gt_labels_onehot.append(label_unique) # [K,num_classes]
# gt_segments is the unique gt_segments
# gt_labels is the one hot encoding for multi class
return gt_segments_unique, gt_labels_onehot
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